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Career Path in Data Science for Data Scientists

Data scientists are considered as one of the top-notch job profiles of this era. Each and every domain of science, technology, arts, and economics are inclined more towards analytics which needs a massive amount of data collected for a long period of time. As per LinkedIn report, there is a vast requirement for professionals who have the capability to mine as well as interpret data. They are none other than BI Analyst, data analyst, data mining engineer and finally data scientists, etc. Hence, this domain has evolved as a new branch of science that involves the understanding of statistics, numerical methods, probability along with the knowledge of computer science (such as programming, Natural Language Processing, SQL, neural network, algorithms, Machine Learning, deep learning, etc.Choosing a career path in data science and acquiring different data scientist skills associated with it can transform your career. But sadly, great career paths followed by jobs do not merely fall out of the sky by simply mastering some subjects and programming languages like Python, R, SQL, statistics, etc. Finding the perfect Data scientist jobs takes time as well as effort along with proper knowledge.So, you have to take each and every subject or domain of data science in parallel to meet the requirement; so that you can efficiently place yourself or end up with a data science career. This article will help you figure out which career option will be the best for you. It discusses the new data science skills and job profiles that are opted by data science enthusiasts for their career. Also, we will get to know which path is right for you.To start a career in data science for ultimately landing yourself into the role of a data scientist, there is no specific learning path as such that you need to follow from your pre-school or high school. But any exclusive degree program or course will prepare you as a professional to start your career path in this data science area. But, here is a list of some courses and subjects that are beneficial and will give weight and accelerate your career goals towards data science.How to become a data scientist:The very first thing you must know is subjects like basics of mathematics and statistics, data computation and how they get stored, and a programming language which can support the concepts of data science efficiently.Also, students opt for UG and PG courses with specialization in Data Science and Machine Learning.There are various online websites that provide paid as well as free courses on data science and can give an in-depth knowledge on various specialized domains of data science such as data visualization using Matplotlib and Pandas library, data cleaning, combining datasets, analyzing data workflow, prediction models and algorithms, Data analysis using Pandas, NumPy, SciPy libraries, dealing with multi-table databases using SQL, acquiring data using Web Scraping, statistical data analysis, machine learning approaches, Calculus for ML, Linear algebra for ML, Linear Regression with ML, implementation of decision trees, Natural Language Processing, concepts of deep learning in data science, and use of other tools like Kaggle, Tableau, Apache Spark etc.There are scholars who opt for PG courses (both full-time as well as part-time) on data science to get an in-depth understanding of all the subdomains of this subject and to land themselves with a good profile in any reputed company.So, let’s dig deep into the journey of career tracking from initial data science role to a data scientist. First of all, we will make you familiar with the different job titles and profiles opted by data science aspirants, along with their descriptions, which will help you contrast with the different options that data science career can provide you with. You will also get to know some of the unusual in-depth understanding of different Data scientist career paths that you may not have thought about.1. Data analyst: This position is considered as an entry-level position in the data science field and its growth can lead you to a data scientist role with promotions and experience. But, you must have a clear idea that each and every data analyst do not come under the same work level and that their salary package range accordingly from junior level and interns to expert professional analysts.They are primarily engaged in analyzing a company or firm’s data and use such data to answer business questions and predict product growth. Based on such analysis, the company takes proactive actions. A sample scenario can be like this, a senior associate might ask a data analyst to look at the sales data received from the marketing campaign for assessing its efficiency and to recognize its strengths as well as weaknesses. Such work would engross accessing of company’s sales data, perhaps cleaning it, and then performing some statistic analysis on those data for predicting the business outcome, and finally visualizing as well as communicating those outcomes using data visualization programs.The various skills required in this role are –Implementing algorithms using Python or R library packages for transitional data science programmingData cleansingUnderstanding the concepts of probability and statisticsIntermediate SQL queriesVisualizing dataConverse clearly the different complex data analysis for making people understand who do not possess programming or statistics knowledge.2. Data Engineer: These engineers are hired for managing the infrastructure of a company’s data more technically. They are less involved in statistical analysis and invest their development effort more towards software development along with programming abilities. From this job role, one can become a data scientist with work experience.Companies hire data engineers specifically to develop tools and algorithms that can be used to build data pipelines for automating the most up-to-date marketing, sales, and/or profit or returns’ data so that the data analysts or data scientists can rapidly analyze or investigate data in a utilizable format. These data engineers are also responsible for developing and maintaining the application infrastructure required for storing and fast accessing of previously fed data.3. Business Intelligence Analyst: Companies are now hiring  Business Analysts who make use of different data for figuring out trends in marketing and trade by analyzing data for developing an apparent picture of which company products are in lead and which have less demand. Also, they focus on generating analytics for their competitor’s market position to compare and stay a step ahead in the competitive mark4. Data Mining Engineer: They are hired for examining not only the data for the company but also for other third-party clients whose project are also taken up. Other than analyzing different collections of data, the data mining engineer is also responsible for creating efficient algorithms for helping other data analysts make their task easier and well-organized.5. Data Architect: They are a special team of professionals who work closely with company’s clients, data mining engineers, developers and system designers for creating an outline sketch, which data management systems will utilize for integrating, concentrating maintaining, as well as protecting various data sources.6. Data Scientist: They are senior data science experts (who used to be any one of the above) who have gathered experience in this domain. They perform different types of work similar to that of data analysts and are also normally involved in building machine learning models for making accurate predictions about the company’s future business and marketing strategies. But on a good note, the data scientists who work in companies often have extra liberty to practice their individual ideas and thoughts as well as to conduct research for discovering appealing prototypes and trends in the data that is beneficial for the firm or has some new approach with high productivity.Data scientists are also involved in interpreting a business assignment into an analytics program, developing constructive hypotheses, or understanding any series of data, or patterns in raw data for measuring the impact these data will pose on the company’s businesses. They have the freedom to choose their own algorithm or create new approaches to solve a particular task. They use business analytics not only for forecasting data but also for providing the company with meaningful solutions so that their research (algorithms) can increase the revenue of the company.7. Senior Data Scientist: They are senior-most or next-level data scientists who have the capability to foresee what a business’s future needs and how the market is turning its needs. They analyze critical data for actionable intelligence. Their task is not only to gather data but also engages in analyzing those huge data sets for determining extremely complex production problems in an efficient manner. Data scientists usually mine as well as analyze data that has a diverse spectrum like–customer transactions, sensor data, clickstreams, social media data, different corporate log files and GPS plots. They have 10 to 15 years of expertise in their domain, which profits the company as they generate different means of using statistical data or create their own development tools, models and algorithms for efficient data handling and predictions.Other Specialized Job titles and career paths which can lead to data scientists or senior-level data science experts –1. Machine Learning Engineer: There are many organizations or firms who interchangeably use machine learning engineers with data scientists, where some other firms consider them as a data scientist who is specialized in the machine learning domain. Also, there are many companies where “machine learning engineers” are more a blend of programmers or developers who are actively involved in taking the analysis of a data scientist and incorporate those data into deployable software. But in general, all ML engineers have a moderate or good level of programming skills and a very advanced understanding of ML techniques and algorithms.2. Quantitative Analyst: They are also abbreviated as “quants”. They are hired for advanced statistical analyses or for calculating and answering to company’s future needs and make predictions associated with sales, finance or even risk. They also have a common understanding of the machine learning models which can calculate statistical data and crack the company’s economic problems and foresee markets needs and trends.3. Data Warehouse Architect: These professionals are a particular division of experts within the data engineering domain and they are charged to hold the firm’s data storage unit. This role highly needs SQL and understanding of database systems as a major skill, as well as comprehend the different data science domains, as you might need to work with data analysts or data architect.4. Business Intelligence Analyst:A business analyst is basically a data analyst with expertise in analyzing business and sales trends. This role sometimes also needs familiarity and awareness in working with software-based data analysis tools (like Microsoft Power BI) or data visualization tools (like Tableau). Again, these BI Analysts also need skills that data science analysts have because such skills are also necessary for these positions, along with Python or R programming skills.5. Statistician: They are nowadays called junior data scientists. They are highly skilled statisticians with a solid understanding of probability and statistics. They also need to know statistical programming languages (like R programming). As data scientists, they may not know how to build and train using machine learning algorithm and models, but they are much more familiar with the mathematical logic that is lying under the machine learning models.6. Marketing Analyst: Marketing analysts monitor and analyze the data related to sales & marketing for assessing as well as improving the efficiency of marketing operations. In the modern era of digital marketing, these analysts are hired to analyze a progressively large quantity of company’s data and handle software solutions such as Google Analytics which allows its users to analyze company’s marketing approaches without any programming skills. These professionals also need to have sound knowledge of statistics.7. Operations Analyst: Operations analysts are those who are involved in probing and reforming a business’s in-house processes. Not all operations analyst role requires the data skills, but in a lot of situations, being capable of cleaning, analyzing, as well as visualizing data makes it significant for determining the smooth working of the company.8. Data science internship: If you are in search of a data science role where you will experience on-the-job learning as an entry-level role, then joining as an intern can help you learn as well as get a hands-on experience on the applied part of learning. So, an internship is a great option if you are a fresher or a student and wants to give a kick-start in this domain. The internship in this field varies–some are paid internships while others are unpaid and typically run for a short time period–usually, 3 to 6 months and there is no guarantee of permanent employment in the end. Once you gain momentum in this domain by gathering experience, an intern can become a data analyst and then senior data analyst or data science engineer, and finally reach the level of a data scientist as well.Data Scientist - Job listing in different websitesSourceFreelance data analysts or data scientists:So far, you have seen various roles associated with data science that are full-time career options. If you are not looking for a full-time professional job option, then as an alternative you can opt for a freelance role also. It is not always that companies hire full-time data science analysts and data scientists. Companies that grow new interests in this domain hire data scientists and assign them a few freelance projects before committing to permanent job roles in their companies. Again, freelance data scientists are also essential in situations where companies with permanent or established data science team of experts need extra help for saving time or meeting the deadline. These are all potential clients who need a helping hand of freelance data scientists.Till now, you have encountered all the possible ways that can lead to a data scientist career in corporate grounds. There is another broader branch of data science that belongs exclusively from an academic point of view and can trail your career to data scientist. So, from the academic point of view, you can also choose your career option as a data science professor or lecturer in universities or institutes and train yourself to acquire the position of a data scientist.Doing research in this particular domain can also help academic scholars get a good salary package from their respective universities. These researchers can take their data science work to the next level and patent their research methodologies. These researchers also have the opportunity to switch roles from academic research and scientist role to corporate research and development profile. There is also an opportunity to join the different developers’ community of data science tools or technologies or otherwise library design programs.The role of data scientists does not end here. There are various other fields where these experts are spreading their influence and applications. Board members of different sports like cricket, football, hockey, etc. are also hiring data scientists or data analysts who can calculate the efficiency of players and analyze their gameplay and scores as well as predict other such relevant data. In the field of medical science, different data related to patients or research need adequate analysis before operating them to a functional level.Here is the list of workplaces or opportunities where different data science experts can set up their career path with a job based on their work taste and interests –i. Handling servers and data warehouses: Data Engineers, Database Developers, Data Analysts.ii. Data mining and statistical analytics department: Data Scientists, Business Analysts, data science statisticians.iii. Cloud computing and distributed computing: Data Architect (Cloud computing specialist), Cloud Data Engineers, Platform Engineers.iv. Database management and architecture: DBA, Data Specialist.v. Business Intelligence and Strategy-making department: BI Data Engineer, BI Analyst, Quantitative Analysts, Data Strategist, Data Scientists.vi. ML / Cognitive Computing Department: Machine Learning Engineer, AI Specialist, Cognitive Developer and Analysts, Data Science researchers, Data Scientists.vii. Data Visualization and Presentation unit: Data Analysts, Data Visualization Engineers, Operations analysts.viii. Data analytics (related to logistics, technology, sales, financials, human resources, etc.): Marketing analysts, Operations analysts, Data Scientists.ix. Sector-specific data science experts (Healthcare, Insurance, sports, etc.): Data Analyst, Business Analyst, Data Scientist, etc.Once you get a role as a data scientist in any organization or corporate, you will meet with a group of professionals who are from a diverse academic background and other professionals within your team whom you may need to guide or mentor, and lead the team to accomplish the target of the project. In the career path from a simple data science analyst to a data scientist, the working area you will opt for will most likely be interrelated to your academic background, knowledge or interests that you have in parallel fields of learning. Lastly, it can be said that data scientists possess an exceptional blend of technical skills, analytical skills, and business wisdom required for effectual analysis of massive datasets although thinking significantly for ultimately converting raw intelligence into crisp and achieving insight.
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Career Path in Data Science for Data Scientists 5656 Career Path in Data Science for Data Scientists Blog
Susan May 13 Aug 2019
Data scientists are considered as one of the top-notch job profiles of this era. Each and every domain of science, technology, arts, and economics are inclined more towards analytics which needs a mas...
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Data Science Vs Machine Learning Vs Artificial Intelligence

What is Data Science?Data Science is an interdisciplinary field whose primary objective is the extraction of meaningful knowledge and insights from data. These insights are extracted with the help of various mathematical and Machine Learning-based algorithms. Hence, Machine Learning is a key element of Data Science.Alongside Machine Learning, as the name suggests, “data” itself is the fuel for Data Science. Without the availability of appropriate data, key insights cannot be extracted from it. Both the volume and accuracy of data matters in this field, since the algorithms are designed to “learn” with “experience”, which comes through the data provided. Data Science involves the use of various types of data, from multiple sources. Some of the types of data are image data, text data, video data, time-dependent data, time-independent data, audio data, etc.Data Science requires knowledge of multiple disciplines. As shown in the figure, it is a combination of Mathematics and Statistics, Computer Science skills and Domain Specific Knowledge. Without a mastery of all these sub-domains, the grasp on Data Science will be incomplete.  What is Machine Learning?Machine Learning is a subset or a part of Artificial Intelligence. It primarily involves the scientific study of algorithmic, mathematical, and statistical models which performs a specific task by analyzing data, without any explicit step-by-step instructions, by relying on patterns and inference, which is drawn from the data. This also contributes to its alias, Pattern Recognition.Its objective is to recognize patterns in a given data and draw inferences, which allows it to perform a similar task on similar but unseen data. These two separate sets of data are known as the “Training Set” and “Testing Set” respectively.Machine Learning primarily finds its applications in solving complex problems, which, a normal procedure oriented program cannot solve, or in places where there are too many variables that need to be explicitly programmed, which is not feasible.As shown in the figure, Machine Learning is primarily of three types, namely: Supervised Learning, Unsupervised Learning and Reinforcement Learning.Supervised Learning: This is the most commonly used form of machine learning and is widely used across the industry. In fact, most of the problems that are solved by Machine Learning belong to Supervised Learning. A learning problem is known as supervised learning when the data is in the form of feature-label pairs. In other words, the algorithm is trained on data where the ground truth is known. This is learning with a teacher. Two common types of supervised learning are:Classification: This is a process where the dataset is categorized into discrete values or categories. For example, if the input to the algorithm is an image of a dog or a cat, ideally, a well-trained algorithm should be able to predict whether the input image is that of a dog or of a cat.Regression: This is a process where the dataset has continuous valued target values. That is, the output of the function is not categories, but is a continuous value. For example, algorithms that forecast the future price of the stock market would output a continuous value (like 34.84, etc.) for a given set of inputs.  Unsupervised Learning: This is a much lesser used, but quite important learning technique. This technique is primarily used when there is unlabeled data or data without the target values mentioned. In such learning, the algorithm has to analyze the data itself and bring out insights based on certain common traits or features in the dataset. This is learning without a teacher. Two common types of unsupervised learning are:Clustering: Clustering is a well known unsupervised learning technique where similar data are automatically grouped together by the algorithm based on common features or traits (eg. color, values, similarity, difference, etc.).Dimensionality Reduction: Yet another popular unsupervised learning is dimensionality reduction. The dataset that is used for machine learning is often huge and of high dimensions (higher than three dimensions). One major problem in working with high dimensional data is data-visualization. Since we can visualize and understand up-to 3 dimensions, higher dimensional data is often difficult for human beings to interpret. In addition to this, higher dimension means more features, which in turn means a more complex model, which is often a curse for any machine learning model. The aim is to keep the simplest model that works best on a wide range of unseen data. Hence, dimensionality reduction is an important part of working with high dimensional data. One of the most common methods of dimensionality reduction is Principal Component Analysis (PCA).Reinforcement Learning: This is a completely different approach to “learning” when compared to the previous two categories. This particular class of learning algorithms primarily finds its applications in Game AI, Robotics and Automatic Trading Bots. Here, the machine is not provided with a huge amount of data. Instead, in a given scenario (playground) some parameters and constrictions are defined and the algorithm is let loose. The only feedback given to the algorithm is that, if it wins or performs a correct task, it is rewarded. If it loses or performs an incorrect task, it is penalized. Based on this minimal feedback, over time the algorithm learns to how to do the correct task on its own.What is Artificial Intelligence?Artificial Intelligence is a vast field made up of multidisciplinary subjects, which aims to artificially create “intelligence” to machines, similar to that displayed by humans and animals. The term is used to describe machines that mimic cognitive functions such as learning and problem-solving.Artificial Intelligence can be broadly classified into three parts: Analytical AI, Human-Inspired AI, and Humanized AI.Analytical AI: It only has characteristics which are consistent with Cognitive Intelligence. It generates a cognitive representation of the world around it based on past experiences, which inspires future decisions.Human-Inspired AI: In addition to having Cognitive Intelligence, this class of AI also has Emotional Intelligence. It has a deeper understanding of human emotions in addition to Cognitive Intelligence and thus has a better understanding of the world around it. Both Cognitive Intelligence and Emotional Intelligence contributes to the decision making of Human-Inspired AI.Humanized AI: This is the most superior form of AI among the three. This form of AI incorporates Cognitive Intelligence, Emotional Intelligence, and Social Intelligence into its decision making. With a broader understanding of the world around it, this form of AI is able to make self-conscious and self-aware decisions and interactions with the external world.How are they interrelated?From the above introductions, it may seem that these fields are not related to each other. However, that is not the case. Each of these three fields is quite closely related to each other than it may seem.If we look at Venn Diagrams, Artificial Intelligence, Machine Learning and Data Science are overlapping sets, with Machine Learning being a subset or a part of Artificial Intelligence, and Data Science having a significant chunk of it under Artificial Intelligence and Machine Learning.Artificial Intelligence is a much broader field and it incorporates most of the other intelligence-related fields of study. Machine Learning, being a part of AI, deals with the algorithmic learning and inference based on data, and finally, Data Science is primarily based on statistics, probability theory, and has significant contribution of Machine Learning to it; of course, AI also being a part of it, since Machine Learning is indeed a subset of Artificial Intelligence.Similarities: All of the three fields have one thing in common, Machine Learning. Each of these is heavily dependent on Machine Learning Algorithms.In Data Science, the statistical algorithms that are used are limited to certain applications. In most cases, Data Scientists rely on Machine Learning techniques to extract inferences from data.The current technological advancement in Artificial Intelligence is heavily based on Machine Learning. The part of AI without Machine Learning is like a car without an engine. However, without the “learning” part, Artificial Intelligence is basically Expert Systems, Search and Optimization algorithms.Difference between the threeEven though they are significantly similar to each other, there are still a few key differences that are to be noted.Data ScienceMachine LearningArtificial IntelligenceThe main goal is the analysis of data and drawing meaningful insights from it through statistical and algorithmic methods.The main goal is to recognize the pattern in data through algorithms that “learn” from the given data and perform well on unseen data.The main goal is to achieve “intelligence” to machines, such that they are socially, emotionally and logically aware of their surroundings.Machine Learning, Statistics, and Probability theory are the core building blocks of it.It is one of the fundamental technologies that fuel other fields. Primarily based on the fields of study like Calculus, Linear Algebra, and Deep Learning.Machine Learning, Expert Systems, Search and Optimization Algorithms, Statistics, Probability, Linear Algebra, and Calculus are the basic building blocks of AI.Very common in terms of Job Profile.Less common in terms of Job Profile.Very rarely do job profiles ask for Artificial Intelligence.This is a commercial and research-oriented domain.This is both a commercial and research-oriented domain.This is more of a research-oriented domain.ApplicationsSince all the three domains are interrelated, they have some common applications and some unique to each of them. Most applications involve the use of Machine Learning in some form or the other. Even then, there are certain applications of each domain, which are unique. A few of them are listed below:Data Science: The applications in this domain are dependent on machine learning and mathematical algorithms, such as statistics and probability based algorithms.Time Series Forecasting: This is a very important application of data science and is used across the industry, primarily in the banking sector and the stock market sector. Even though there are Machine Learning based algorithms for this specific application, Data Scientists usually prefer the statistical approach.Recommendation Engines: This is a statistics-based approach towards recommending products or services to the user, based on data of his/her previous interests. Similar to the previous application, Machine Learning based algorithms to achieve similar or better results is also present.Machine Learning: The applications of this domain is nearly limitless. Every industry has some problem that can partially or fully be solved by Machine Learning techniques. Even Data Science and Artificial Intelligence roles make use of Machine Learning to solve a huge set of problems.Computer Vision: This is another sub-field which falls under Machine Learning and deals with visual information. This field itself finds its applications in many industries, for example, Autonomous Driving Vehicles, Medical Imaging, Autonomous Surveillance Systems, etc.Natural Language Processing: Similar to the previous example, this field is also self-contained sub-field of research. Natural Language Processing (NLP) or Natural Language Understanding (NLU) primarily deals with the interpretation and understanding of the meaning behind spoken or written text/language. Understanding the exact meaning of a sentence is quite difficult (even for human beings). Teaching a machine to understand the meaning behind a text is even more challenging. Few of the major applications of this sub-field are the development of intelligent chatbots, artificial voice assistants (Google Assistant, Siri, Alexa, etc.), spam detection, hate speech detection and so on.Artificial Intelligence: Most of the current advancements and applications in this domain is based on a sub-field of Machine Learning, known as Deep Learning. Deep Learning deals with artificially emulating the structure and function of the biological neuron. However, since few of the applications of Deep Learning have already been discussed under Machine Learning, let us look at applications of Artificial Intelligence that is not primarily dependent on Machine Learning.Game AI: Game AI is an interesting application of Artificial Intelligence, where the machine automatically learns to play complex games to the level where it can challenge and even win against a human being. Google’s DeepMind had developed a Game AI called AlphaGo, which outperformed and beat the human world champion in 2017. Similarly, video game AI’s have been developed to play Dota 2, flappy bird and Mario. These models are developed using several algorithms like Search and Optimization, Generative Models, Reinforcement Learning, etc.Search: Artificial Intelligence has found several applications in Search Engines, for example, Google and Bing Search. The method of displaying results and the order in which results are displayed are based on algorithms developed in the field of Artificial Intelligence. These applications do contain Machine Learning techniques, but their older versions were developed by algorithms like Google’s proprietary PageRank Algorithm, which were not based on “Learning”.Robotics: One of the major applications of Artificial Intelligence is in the field of robotics. Teaching robots to walk/run automatically (for example, Spot and Atlas) using Reinforcement Learning has been one of the biggest goals of companies like Boston Dynamics. In addition to that, humanoid robots like Sophia are a perfect example of AI being applied for Humanized AI.Skill-set RequiredSince the fields are interrelated by a significant degree, the skill-set required to master each of these fields is nearly the same and overlapping. However, there are a few skill-sets that are uniquely associated with each of them. The same has been discussed further.Mathematics: Each of these fields is math heavy, which means mathematics are the basic building blocks of these fields and in order to fully understand the algorithms and master them, a great math background is necessary. However, all the fields of math are not necessary for all of these. The specific fields of math that are required are discussed below:Linear Algebra: Since all of these fields are based on data, which comes in huge volumes of rows and columns, matrices are the easiest and most convenient method of representing and manipulating such data. Hence, a thorough knowledge of Linear Algebra and Matrix operations is necessary for all three fields.Calculus: Deep Learning, the sub-field of Machine Learning is heavily dependent on calculus. To be more precise, multivariate derivatives. In neural networks, backpropagation algorithms require multiple derivative calculations, which demands a thorough knowledge of calculus.Statistics: Since these fields deal with a huge amount of data, the knowledge of statistics is imperative. Statistical methods to deal with the selection and testing of smaller sample size with diversity is the common application for all three fields. However, statistics finds its main application in Data Science, where most of the algorithms are purely based on statistics (eg. ARIMA algorithm used for Time Series Analysis).Probability: Similar to the reason behind statistics, probability and the conditional probability of a certain event is the basic building block of important Machine Learning algorithms like Naive Bayes Classifier. Probability theory is also very important in understanding Data Science Algorithms.Computer Science: There is no doubt about either of these fields being a part of the Computer Science field. Hence, a thorough knowledge of computer science algorithms is quite necessary.Search and Optimization Algorithms: Fundamental Search Algorithms like Breadth-First Search (BFS), Depth-First Search (DFS), Bidirectional Search, Route Optimization Algorithms, etc. are quite important. These search and optimization algorithms find their use in the Artificial Intelligence field.Fuzzy Logic: Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. It imitates the way human beings make decisions. For example, making a YES or NO decision based on a certain set of events or environmental conditions. Fuzzy Logic is primarily used in Artificially Intelligent Systems.Basic Algorithms and Optimization: Even though this is not a necessity, but it is a good-to-have knowledge since fundamental knowledge on algorithms (searching, sorting, recursion, etc.) and optimization (space and time complexity) is necessary for any computer science related fields.Programming Knowledge: The implementation of any of the algorithms in these fields is through programming. Hence a thorough knowledge of programming is a necessity. Some of the most commonly used programming languages are discussed further.Python: One of the most commonly used programming languages for either of these fields is Python. It is used across the industry and has support for a plethora of open source libraries for Machine Learning, Deep Learning, Artificial Intelligence, and Data Science. However, programming is not just about writing code, it is about writing proper Pythonic code. This has been discussed in detail in this article: A Guide to Best Python Practices.R: This is the second most used programming language for such applications across the industry. R excels in statistical libraries and data visualization when compared to python. However, lacks significantly when it comes to Deep Learning libraries. Hence, R is a preferred tool for Data Scientists.Job MarketThe Job Market for each of these fields is in very high demand. As a direct quote from Andrew Ng says, “AI is the new Electricity”. This is quite true as the extended field of Artificial Intelligence is at the verge of revolutionizing every industry in ways that could not be anticipated earlier.Hence, the demand for jobs in the field of Data Science and Machine Learning is quite high. There are more job openings worldwide than the number of qualified Engineers who are eligible to fill that position. Hence, due to supply-demand constraints, the amount of compensation offered by companies for such roles exceeds any other domain.The job scenario for each of the different domains are discussed further:Data Science: The number of job posting with the profile of Data Science is highest, among the three discussed domains. Data Scientists are handsomely paid for their work. Due to the blurred lines in terms of the difference between the fields, the job description of a Data Scientist ranges from Time Series Forecasting to Computer Vision. It basically covers the entire domain. For further insights on the job aspect of Data Science, the article on What is Data Science can be referred to.Machine Learning: Even though the number of jobs postings having the job profile as “Machine Learning Engineer” is much lesser when compared to that of a Data Scientist, it is still a significant field to consider when it comes to availability of jobs. Moreover, someone who is skilled in Machine Learning is a good candidate to consider for a Data Science role. However, unlike Data Science, Machine Learning job descriptions primarily deal with the requirements of “Learning” algorithms (including Deep Learning), and the industry ranges from Natural Language Processing to developing Recommendation Engines.Artificial Intelligence: Coming across job postings with profiles of “Artificial Intelligence Developer” developer is quite rare. Instead of “Artificial Intelligence”, most companies write “Data Scientists” or “Machine/Deep Learning Engineers” in the job profile. However, Artificial Intelligence Developers, in addition to getting jobs in the Machine Learning domain, mostly find jobs in Robotics and AI R&D oriented companies like Boston Dynamics, DeepMind, OpenAI, etc.ConclusionData Science, Machine Learning and Artificial Intelligence are like the different branches of the same tree. They are highly overlapping and there is no clear boundary amongst them. They have common skill set requirements and common applications as well. They are just different names given to slightly different versions of  AI.Finally, it is worth mentioning that since there is high overlap in required skill-set, an optimally skilled Engineer is eligible to work in either of the three domains and switch domains without any major changes.
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 Data Science Vs Machine Learning Vs Artificial Intelligence

Data Science Vs Machine Learning Vs Artificial Intelligence

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What is Data Science?Data Science is an interdisciplinary field whose primary objective is the extraction of meaningful knowledge and insights from data. These insights are extracted with the help of ...
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Node Js Best Practices

A brief introduction to NodeJS, its history, usage, and popularity in the current web world.BEST PRACTICESStructure the Project Break down the project into folders and filesStructure the app according to the API endpointsDon’t make one huge fileUse Code FormattersUse Prettier or BeautifyAvoid anonymous functionsCreate reusable functionsUse camelCaseFollow a naming convention for methods, objects, and variablesAvoid callbacks - Use async-awaitDo not run onto the callback hellHandle errors - Use try-catchUse a loggerUse a process monitorUse environment variablesAlways use LTSPrevent XSS attacksEscape HTML, JS and CSS scripts for special characters.Avoid JS evalLearn to crash - Dodge DOS attacksUse ESLintCatch anti-patterns and follow standardsIntroduction To NodejsNodeJS is an open-source, cross-platform JavaScript run-time environment that executes JavaScript code outside of a browser. That is the official definition of NodeJS. But in simpler words, NodeJS allows you to write and execute JavaScript code on a server or inside an application that does not have to work inside the browser. NodeJS is primarily used to write system utilities and web servers.In the recent few years, NodeJS has become tremendously popular and is one of the most popular runtime environments for web servers. It is an open source, a cross-platform runtime environment for developing server-side applications, or more precisely, code that run on a server.NodeJS is a platform built on Chrome's JavaScript runtime for easily building fast and scalable network applications. It uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices.In this article, we are going to have a look at some of the best practices that developers should follow to keep their huge NodeJS projects maintainable.BEST PRACTICESStructure the ProjectStructuring the code base is very important to projects that scale and involve large teams. This ensures that the code is manageable and developers across teams can understand the code. For this, there is a need to establish some ground rules that will be followed by all developers and even testers. As the project grows, the code needs to be broken down, both logically and physically. This ensures that as the project scales up, it stays manageable. A few guidelines as to how to break down a huge code base into smaller and manageable chunks are as following.The biggest hurdle of large projects is that they tend to grow with each new feature or bug fix. This requires that the project be broken down into different files and then structure those files into directories. Now, this can vary from project to project but the concept is that the project needs to organized such that to make a small change or to add a small feature to one part of the codebase, the developer only needs to navigate to one branch of the directory tree.Bad structureGood structuresrcuser_login.jscancel_order.hsupdate_order.jscreate_order.jssend_order_email.jscreate_account.jsverify_account.jsuser_login.jssrcorderscancel_order.hsupdate_order.jscreate_order.jsemailssend_order_email.jsaccountscreate_account.jsverify_account.jsauthuser_login.jsWith large codebases come a huge set of dependencies. Having one big software with many dependencies is just hard to reason about and this often leads to spaghetti code which is touch or almost impossible to manage. The ultimate solution is to break down the beast into smaller and tamable beasts. This calls to divide the whole stack into self-contained components that don't share files with others, each constitutes very few files and therefore becomes much more manageable. Also, changes to one are completely opaque to another.This opacity ensures that changes to one module or component in the project do not break the other ones that in some way are using that module or component.Use code formattersThis one is not really specific to NodeJS but tools like Prettier and Beautify improve the visual aesthetics of the codebase which can otherwise look very messy and stressing. These help you quickly find and fix common syntactical errors. Although it is more about the visual aesthetics of the code, these tools really help as they work constantly as you code. They work according to the language you are coding in (JavaScript or TypeScript) and then color certain keywords, format the code and display common errors like missing braces or semicolons.Most of the popular code formatters support popular Code Editors as well. For example, both Prettier and Beautify support Visual Studio Code which is my personal favorite when NodeJS is concerned. Prettier has a VSCode extension called prettier-vscode and unibeautify-vscode.The official extensions are totally configurable and auto-format the code when the file is saved thereby saving a lot of time and ensure that developers do not lose focus.Not using any of these code formatters will not affect the coding efficiency in any way but it definitely affects the developers’ efficiency. Developers will focus on tedious spacing and line-width concerns and time might be wasted overthinking the project's code style.Avoid Anonymous FunctionsAccording to this principle, it is recommended to have named functions over anonymous inline functions. It suggests you to always create named functions, including closures and callbacks and name them logically. An example would be more appropriate for explaining this. Let’s have a look at an example.Inline anonymous functionNamed reusable functionvar woocommerce = apapi.createOrder(orderData).then((order) => {  // code to create order  . . .  // code to send order email  . . .  // code to send response to client  . . .})var woocommerce = apapi.createOrder(orderData).then((order) => {   postOrderTasks();})Function postOrderTasks(order) {  // code to create order  . . .  // code to send order email  . . .  // code to send response to client  . . .}In the code snippet on the left in the above set, you can see that once the createOrder method is initiated, it executes certain operations in a certain order but it all happens inside an anonymous function. In the code on the right, instead of creating an anonymous function, we create a named function called postOrderTasks() that only does those tasks in the same order. The primary advantage here is that this function can be re-used over an order again in other parts of the project as well and this reduces code redundancy.Moreover, if you find a bug in the code that creates an order, you fix that bug only in once place i.e. postOrderTasks() method and it will reflect in all places - saves time.Another advantage of using named functions is that when you are profiling a NodeJS app, it allows you to easily understand what you're looking at when checking a memory snapshot for memory leaks or inefficient code. Debugging production issues using a core dump (memory snapshot) might become challenging if you do use too many anonymous functions.Use camelCaseIt is generally considered a good practice to follow a naming convention while naming all variables, objects, and classes but the case of the names and the characters in the names is also very important as lower and upper cases represent different meanings.It is recommended to use lowerCamelCase when naming constants, variables, and functions and UpperCamelCase (capital first letter as well) when naming classes.This is universally accepted conventions and developers across the globe can easily identify and differentiate objects and classes when using this naming convention.Article article = new Article();Article firstArticle = new Article();Avoid callbacks - Use async-awaitNodeJS 8 and higher have to fill Async-await support in their LTS releases. It is often a good practice to have async-await operators instead of using multiple callbacks. Inspired by the C#’s async-await operators, NodeJS offers Async-await operators that appear to block asynchronous operations waiting for the results before continuing with the following statement where synchronous execution is a requirement.Async-await in NodeJS is completely non-blocking although it may appear at blocking. It is fast and efficient and works very similar to C#’s async-await operators.Let’s have a look at an example. Following the code that uses traditional callback pattern to execute asynchronous synchronously.In the above code block, there are 3 asynchronous functions executing in order and therefore the code looks like a staircase. With more functions, this looks terrible and code readability becomes very poor. Async-await comes to the rescue from callback hell. Now, let’s rewrite the code using the async-await operators.As you can see, the code is so much more readable. The function foo() does not block NodeJS’s event loop, in spite of its synchronous appearance. Execution within the function is suspended during each of its three asynchronous operations, but NodeJS's event loop can execute other code whilst those operations are being executed.Using the await operator next to an asynchronous function call ensures that the next line of code will only be executed when the awaited function is executed successfully.It is recommended to use try-catch blocks when using async-await operators which we will discuss next.Avoid callbacks - Use async-awaitThis is more of a requirement than a recommendation. When using async-await operators, we no longer have the access to success and failure callbacks in the promises - then() and catch(). How do we handle errors then? Well, we fall back to the old try-catch method of handling errors in the code.Wrap the code with the await keywords in a try block and correspondingly write the error handling code in the catch block. Simple right? Let’s rewrite the above code block that uses async-await using the try-catch.Simple, elegant and the code stays readable. Keep in mind that if you do not recommend you awaited calls in try blocks, you may not be able to find if you function executed successfully or failed. In such a situation, you will not be able to handle errors.Use a loggerWhether the software is working perfectly or not, logs are an avoidable piece of information. Always use a logger tool that logs information about databases accesses, crashes, user access patterns and other information that might be useful for the teams. There are many sophisticated tools available out there for NodeJS. Some of the most popular ones are -Node-LogglyBunyanWinstonMorganI have used Winston in numerous projects and it is one of the best but it varies from project to project and from developer to developer as well.With Winston, you can:Use multiple means of transportCreate custom transportsPerform profilingHandle exceptionsUse one of a range of pre-defined error levelsCreate custom error levelsMultiple modes of transport allow you to log to files, network, or pretty much anything. There are several core modes of transport included in Winston, which leverage the built-in networking and file I/O offered by Node.js core. In addition, there are additional transports written by members of the community.Without a logger, skimming through console logs or manually through messy text file without querying tools or a decent log viewer can be a pain when your software is down.Use a process monitorThere may be cases when the server crashes or run into errors that it cannot handle since we, as developers, have not considered certain edge cases. In these situations, we need to ensure that the process is terminated gracefully, and restarted immediately without no or minimal downtime. Information about the crash should also be written to logs so that we can handle the crash whenever it occurs in the future or eliminate it altogether.Process monitors or process management tools like PM2 are available for NodeJS that handle all of these things for us. PM2 utility also integrates a load balancer. You can keep application server processes alive and reload/restart them with zero downtime.PM2 allows you to easily manage your application’s logs as well. You can display the logs coming from all your applications in real-time, flush them, and reload them. There are also different ways to configure how PM2 will handle your logs (separated in different files, merged, with timestamp…) without modifying anything in your code.Primary features of PM2 are -Process management including automatic app restarts on failure or system rebootsApplication monitoringDeclarative configuration via JSON fileLog managementBuilt-in cluster modeStartup script generation for *nix systemsSeamless updatesIntegrated module systemYou can also check out forever or supervisor which offer similar features for process monitoring.Use environment variablesEnvironment variables are a fundamental part of developing with NodeJS, allowing your app to behave differently based on the environment you want them to run in. These are what the name implies,  variables stored in the environment in which the code is being executed. These variables can be used to store sensitive information like API keys or secret strings and they can also be used to store other information to indicate if the app is in test, dev or production mode. Based on the values of these variables, different code blocks can be executed to perform different operations or perform operations differently.Environment variables can be used to store app-specific keys, software version and build information, files paths and folder paths, ports and host information etc. When the app is deployed on another server or on the cloud, the environment variables ensure that the app still works but now, since the environment is different, it will work exactly as it is supposed to work. You are saved from the trouble of switching variables before deploying the app.There is a specific package called dotenv that is very popular and can be used to read .env files that you can use to use environment variables. Environment variables ensure that your code is always aware of the environment it is running in and can work in ways as intended.Keep in mind that these files should be ignored when you push the code to version control like Github.Always use LTSIt is always recommended to use the LTS version of the latest available NodeJS version. LTS stands for Long term support which means that no matter what happens, you will get support from the NodeJS team for this version in the future. It is future-proof and therefore is suitable to be used in production.The other variant, which is called Stable version, receive frequent updates, bug fixes and performance improvements which can be breaking at times. If you want the most stable version, use the latest available LTS.According to Rod Vagg from NodeJS  LTS team, the point of establishing an LTS plan for Node is to build on top of an existing stable release cycle by delivering new versions on a predictable schedule that have a clearly defined extended support lifecycle. While this may seem at odds with the open source tradition of “release early, release often”, it is an essential requirement for enterprise application development and operations teams.Prevent XSS AttacksCross-Site Scripting (XSS) attacks are a type of injection, in which malicious scripts are injected into otherwise benign and trusted websites. XSS attacks occur when an attacker uses a web application to send malicious code, generally in the form of a browser side script, to a different end user.In NodeJS, XSS attacks can be prevented by using dedicated modules. There are a lot of modules available for this very purpose. The application should be using secure headers to prevent attackers. These can be configured easily using modules like a helmet. There are other packages like sanitizer and dompurify that ensure that the content is sent down to the client as pure content, and it cannot be evaluated. Basically, this is mitigated by using dedicated libraries that explicitly mark the data as pure content that should never get executed.Another common cause is the JavaScript’s eval(), setTimeout(), setInterval() methods. These methods and new Function() are global functions, often used in NodeJS, which accept a string parameter representing a JavaScript expression, that can be evaluated to perform an operation on the client. The security concern of using these functions is the possibility that untrusted user input might find its way into code execution leading to server compromise, as evaluating user code essentially allows an attacker to perform any actions that you can. It is therefore suggested to refactor code to not rely on the usage of these functions where user input could be passed to the function and executed. Another alternative is to sanitize the user input before passing it to one of these methods.Learn to crash - Dodge DOS attacksThe Node process will crash when errors are not handled. Many best practices even recommend to exit even though an error was caught and got handled. This is because if not crashes, this opens a very sweet attack spot for attackers who recognize what input makes the process crash and repeatedly send the same request.A denial-of-service attack (DoS attack) is a cyber-attack in which the perpetrator seeks to make a machine or network resource unavailable to its intended users by temporarily or indefinitely disrupting services of a host connected to the Internet.There is no one solution to this because there is a human sitting at the attacking end but there are a few things that can help.Alert whenever a process crashes due to an unhandled errorValidate and sanitize the inputAvoid crashing the process due to invalid user inputWrap all routes with a catch and consider not to crash when an error originated within a requestCrashing does not look all bad now, does it?Using ESLintESLint is a standard tool for checking possible code errors and fixing code style issues. It is not only used to identify minor spacing issues but also to detect serious code antipatterns like developers throwing errors without classification or not writing a return statement in the method that is supposed to return something. ESLint can automatically fix code styles, but it is often used with other tools like prettier and beautify.Linting forces developers to follow standard practices and therefore makes development easier and coherent for everyone working on the project. In my personal experience, linting has made me a better developer overall.
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Node Js Best Practices

Node Js Best Practices

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A brief introduction to NodeJS, its history, usage, and popularity in the current web world.BEST PRACTICESStructure the Project Break down the project into folders and filesStructure the app acco...
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Top 25 Python Libraries for Machine Learning

Python and Its EcosystemPython is one of the most widely used languages by Data Scientists and Machine Learning experts across the world. Though there is no shortage of alternatives in the form of languages like R, Julia and others, python has steadily and rightfully gained popularity.Python has more interest over R and Julia consistently over the last 5 yearsSimilar to the Google Trends shown above(the plot is prepared using matplotlib and pytrends), confidence is visible year over year with python featuring way above its peers in the StackOverflow surveys for 2017 and 2018. These trends/surveys are the consequences of ease of use, shorter learning curve, widespread usage, strong community, large number of libraries covering depth and breadth of a number of research and application areas.The amazing popularity might make one think that python is the gold standard for Machine Learning. This might be true to a certain degree yet, python isn’t free from criticism of being slow, issues with multi-threading, etc. It would be wrong to overlook its pitfalls and limitations. A nice article outlining some of the issues:Batteries IncludedIn this article, we will take you through an amazing ecosystem of libraries and projects which make python the go-to choice for Machine Learning. But before we start with the libraries, a small note about its “batteries included” philosophy. The batteries included philosophy refers to the all-powerful standard library which makes your life easier as a programmer.SourceThe standard library (or if we take the liberty to say vanilla python installation), contains a set of easy to use modules for tasks ranging from handling JSONs, making RPC calls, emails, mathematical and statistical operations, regex, OS related operations and so on. All these and many more along with a powerful set of data structures like lists and dictionaries enable us to perform tasks with much more ease as compared to other languages. Checkout the page for standard library for more details: along with a good explanation here.Core Data Handling Libraries:1. NumpyPython has a strong set of data types and data structures. Yet it wasn’t designed for Machine Learning per say. Enter numpy (pronounced as num-pee). Numpy is a data handling library, particularly one which allows us to handle large multi-dimensional arrays along with a huge collection of mathematical operations. The following is a quick snippet of numpy in action.Numpy isn’t just a data handling library known for its capability to handle multidimensional data. It is also known for its speed of execution and vectorization capabilities. It provides MATLAB style functionality and hence requires some learning before you can get comfortable. It is also a core dependency for other majorly used libraries like pandas, matplotlib and so on. It’s documentation itself is a good starting point. Official Link.AdvantagesNumpy isn’t just a library, it is “the library” when it comes to handling multi-dimensional data. The following are some of the goto features that make it special:Matrix (and multi-dimensional array) manipulation capabilities like transpose, reshape,etc.Highly efficient data-structures which boost performance and handle garbage collection with a breeze.Capability to vectorize operation, again improves performance and parallelization capabilities.DownsidesThe major downsides of numpy are:Dependency of non-pythonic environmental entities, i.e. due to its dependency upon Cython and other C/C++ libraries setting up numpy can be a painIts high performance comes at a cost. The data types are native to hardware and not python, thus incurring an overhead when numpy objects have to be transformed back to python equivalent ones and vice-versa.2. PandasThink of relational data, think pandas. Yes, pandas is a python library that provides flexible and expressive data structures (like dataframes and series) for data manipulation. Built on top of numpy, pandas is as fast and yet easier to use.Pandas provides capabilities to read and write data from different sources like CSVs, Excel, SQL Databases, HDFS and many more. It provides functionality to add, update and delete columns, combine or split dataframes/series, handle datetime objects, impute null/missing values, handle time series data, conversion to and from numpy objects and so on. If you are working on a real-world Machine Learning use case, chances are, you would need pandas sooner than later. Similar to numpy, pandas is also an important component of the SciPy or Scientific Python Stack (see for more details. Official Link.AdvantagesExtremely easy to use and with a small learning curve to handle tabular data.Amazing set of utilities to load, transform and write data to multiple formats.Compatible with underlying numpy objects and go to choice for most Machine Learning libraries like scikit-learn, etc.Capability to prepare plots/visualizations out of the box (utilizes matplotlib to prepare different visualization under the hood).DownsidesThe ease of use comes at the cost of higher memory utilization. Pandas creates far too many additional objects to provide quick access and ease of manipulation.Inability to utilize distributed infrastructure. Though pandas can work with formats like HDFS files, it cannot utilize distributed system architecture to improve performance.3. ScipyPronounced as Sigh-Pie, this is one of the most important python libraries of all time. Scipy is a scientific computing library for python. It is also built on top of numpy and is a part of the Scipy Stack.This is yet another behind the scenes library which does a whole lot of heavy lifting. It provides modules/algorithms for linear algebra, integration, image processing, optimizations, clustering, sparse matrix manipulation and many more. Official Link.4. MatplotlibAnother component of the SciPy stack, matplotlib is essentially a visualization library. It works seamlessly with numpy objects (and its high-level derivatives like pandas). Matplotlib provides a MATLAB like plotting environment to prepare high-quality figures/charts for publications, notebooks, web applications and so on.SourceMatplolib is a high customizable low-level library that provides a whole lot of controls and knobs to prepare any type of visualization/figure. Given its low-level nature, it requires a bit of getting used to along with plenty of code to get stuff done. Its well documented and extensible design has allowed a whole list of high-level visualization libraries to be built on top. Some of which, we will discuss in the coming sections. Official Link:AdvantagesExtremely expressive and precise syntax to generate highly customizable plotsCan be easily used inline with Jupyter notebooksDownsidesHeavy reliance on numpy and other Scipy stack librariesHuge learning curve, it requires quite a bit of understanding and practice to use matplotlib.Machine Learning Stars:5. Scikit-LearnDesigned as an extension to the SciPy library, scikit-learn has become the de-facto standard for many of the machine learning tasks. Developed as part of Google Summer of Code project, it has now become a widely contributed open source project with over 1000 contributors.Scikit-learn provides a simple yet powerful fit-transform and predict paradigm to learn from data, transform the data and finally predict. Using this interface, it provides capabilities to prepare classification, regression, clustering and ensemble models. It also provides a multitude of utilities for preprocessing, metrics, model evaluation techniques, etc. Official LinkAdvantagesThe go-to package that has it all for classical Machine Learning algorithmsConsistent and easy to understand interface of fit and transformCapability to prepare pipelines not only helps with rapid prototyping but also quick and reliable deploymentsDownsidesInability to utilize categorical data for algorithms out of the box that support such data types (packages in R have such capabilities)Heavy reliance on the Scipy stack6.  StatsmodelsAs the name suggests, this library adds statistical tools/algorithms in the form of classes and functions to the python world. Built on top of numpy and scipy, Statsmodels provides an extensive list of capabilities in the form of regression models, time series analysis, autoregression and so on.Statsmodels also provides a detailed list of result statistics (even beyond what scikit-learn provides). It integrates nicely with pandas and matplotlib and thus is an important part of any Data Scientist’s toolbox. For people who are familiar and comfortable with R style of programming, Statsmodels also provides R-like formula interface using patsy. Official link.AdvantagesPlugs in the gap for regression and time-series algorithms for the python ecosystemAnalogous to certain R-packages, hence smaller learning curveHuge list of algorithms and utilities to handle regression and time series use-casesDownsidesNot as well documented with examples as sklearnCertain algorithms are buggy with little to no explanation of parameters7.  BoostingBoosting is one of the ensemble methods to develop a strong classifier based on multiple weak-learners (bagging is its counterpart). Scikit-learn is one stop shop for most of your Machine Learning algorithm needs. It provides a good enough list of classification algorithms along with capabilities to build boosted models based on them. It also provides gradient-boosting algorithm out of the box.8. Bagging Vs BoostingOver the years, there have been a number of advancements in terms of improving the vanilla gradient boosting algorithm. The improvements have targeted both, its generalization and speed of execution. To bring these capabilities to python, the following are a few variants of the vanilla algorithm.9. XGBoostOne of the most widely used libraries/algorithms used in various data science competitions and real-world use cases, XGBoost is probably one of the best-known variants.SourceA highly optimized and distributed implementation, XGBoost enables parallel execution and thus provides immense performance improvement over gradient boosted trees. It provides capabilities to execute over distributed frameworks like Hadoop easily. It also has wrappers for R, Java and Julia. Official Link.10. LightGBMAnother distributed and fast variant of GBM (Gradient Boosting Machines), LightGBM is from the house of Microsoft. It is similar to XGBoost in most aspects, barring a few around handling of categorical variables and the sampling process to identify node split. LightGBM uses a novel method called GOSS (Gradient based One Sided Sampling) to identify node split. It also has the capability to utilize GPUs to improve performance. It is reported during some competitions that LightGBM is more memory efficient as compared to XGBoost. Official link11. CatBoostThis Implementation from Yandex research is one of the leading variants of boosted trees. It provides capabilities similar to the two variants discussed above. It claims to be better at handling categorical variables and provides support for multi-GPU training. It is also one of the fastest algorithms when it comes to inference. Official LinkThe three variants/competing implementations discussed above have a lot in common yet have some features better than the rest. To better understand the differences in the algorithms and their inference, check out the following article.12.  ELI5Explain Like I am 5 (years old). Yes, this is what ELI5 stands for. It is great that we know how to develop models for different use cases but is there a way we can understand how does the model infer something? Some algorithms like decision trees are inherently explainable, yet not all of them are (at least not out of the box). ELI5 is one such library which provides the capabilities to debug classifiers and provide an explanation around the predictions.Sample output from of  TextExplainer.It provides wrappers around different libraries like scikit-learn, xgboost, and many more to help understand the predictions. The library utilizes the algorithm described by Ribeiro et. Al called LIME (Local Interpretable Model-Agnostic Explanations) for many of the explainers. Official LinkDeep Learning Frameworks : 13.  TensorflowProbably one of the most popular GitHub repositories and one of the most widely used libraries for both research and production environments. Tensorflow is a symbolic math library which allows differentiable programming, a core concept for many Machine Learning tasks.Tensors are the core concept of this library which are generic mathematical objects to represent vectors, scalers, multi-dimensional arrays, etc.SourceIt supports a range of ML tasks but it is primarily utilized for developing deep neural networks. It is utilized by Google (also developed by them) and a number of technology giants for developing and productionalizing neural networks. Tensorflow has capabilities to not just utilize multi-GPU stacks but also work with specialized TPUs or Tensor Processing Units. It has now evolved into this complete environment of its own with modules to handle core functionality, debugging, visualization, serving, etc. Official Link.AdvantagesIndustry grade package which has a huge community support with frequent bug fixes and improvements at regular intervalsCapability to work with a diverse set of hardware like mobile platforms, web, CPUs and GPUsScalability to handle huge workloads and works out of the boxWell documented features with tons of tutorials and examplesDownsidesLow-level interface makes it difficult to get started, huge learning curveComputation graphs are not easy to get used to (though this has been largely addressed with eager execution in version 2.0)14.  TheanoLet’s just start by saying that Theano is to deep learning what numpy is to machine learning. Theano (now a deprecated project) was one of the first libraries to provide capabilities to manipulate multi-dimensional arrays. It predates Tensorflow and hence isn’t as performant or expressive. Theano has capabilities to utilize GPUs transparently. It is tightly integrated with numpy, provides symbolic differentiation syntax along with various optimization to handle small and large numbers. Before the advent of newer libraries, Theano was the defacto building block for working with neural networks. Theano was developed and maintained actively by of Montreal Institute for Learning Algorithms (MILA), University of Montreal until 2017. Official LinkAdvantagesEase of understanding due to its tight coupling with numpyCapability to utilize GPUs transparentlyBeing one of the first deep learning libraries, it has a huge community to help and support issuesDownsidesOnce the workhorse for deep learning use-cases, is now a deprecated project which will not be further developedIts low-level APIs often presented a steep learning curve15.  PyTorchPyTorch is a result of research and development at Facebook’s artificial intelligence group. The current day PyTorch is a merged project between pytorch and caffe2. PyTorch is a python first deep learning framework unlike some of the other well-known ones which are written in C/C++ and have bindings/wrappers for python. This python first strategy allows PyTorch to have numpy like syntax and capability to work seamlessly with similar libraries and their data structures.It supports dynamic graphs and eager execution (it was the only one until Tensorflow 2.0). Similar to other frameworks in this space, PyTorch can also leverage GPUs and acceleration libraries like  Intel-MKL. It also claims to have minimal overhead and hence is supposedly faster than the rest. Official LinkAdvantagesOne of the fastest deep learning frameworks.Capability to handle dynamic graphs as opposed to static ones used by most counterpartsPythonic implementation helps in seamless integration with python objects and numpy like syntaxDownsidesStill gaining ground and support, thus lags in terms of material(tutorials, examples, etc.) to learn from.Limited capabilities like visualizations and debugging as compared to a complete suite in the form of tensorboard for tensorflow.16.   KerasThink simplicity, think Keras. Keras is a high-level Deep Learning framework which has eased the way we develop and work with deep neural networks. Developed primarily in python, it rests on the shoulders of giants like Theano, Tensorflow, and MXNet (also called as backends). Keras utilizes these backends to do the heavy lifting while transparently allowing us to think in terms of layers. For Keras, the basic building block is a layer. Since, most neural networks are different configurations of layers, working in such a manner eases the workflow immensely.A typical Keras based Feed Forward Neural NetworkKeras was developed independently by François Chollet for one of the research projects and has since been integrated as part of Tensorflow as well (though it continues to be developed actively and separately as well). Apart from providing an easy to use interface, it allows provides APIs to work with pre-trained state of the art models like RESNET, AlexNET, VGG and many more.AdvantagesEasy to understand and intuitive interface helps in rapid prototypingHuge number of pre-trained models available for use out of the boxCapability to work with different low-level libraries like tensorflow, theano, mxnet, etc.DownsidesBeing a high-level library makes it difficult to develop custom components/loss functions (though it provides capabilities to extend)Performance is dependent on the underlying backend being used.Others DL Frameworks/LibrariesTensorflow, PyTorch, Theano and Keras are staple libraries when it comes to Deep Learning. These aren’t the only ones though. There are a number of other widely used libraries as well. Each born out of a specific need or due to issues with the popular ones. The following are a few more Deep Learning libraries in python:17.  FastAiThis a high-level library (similar to keras) built on top of PyTorch. As the name suggests, it enables the development of fast and accurate neural networks. It provides consistent APIs and built-in support for vision/image, text, etc.Official Link.18.  CaffeCaffe or Convolutional Architecture for Fast Feature Embedding is a deep learning framework developed by Yangqing Jia for his PhD thesis. It was primarily used/designed for image classification and related tasks though it supports other architectures including LSTMs and Fully Connected ones as well. Official Link19. Apache MXNet One of the most widely used libraries when it comes to image related use cases (see CNNs). Though it requires a bit more boilerplate code but its performance makes up for it. Official Link20.  GluonGluon is a high-level deep learning library/api from AWS and Microsoft. It is currently available through Apache MXNet and allows for ease of use of AWS and Microsoft Azure clouds. It is designed to be developer friendly, fast and consistent. Official Link.NLP Libraries21. NLTKThe Natural Language ToolKit or NLTK is a suite of offerings from the University of Pennsylvania for different NLP or Natural Language Processing tasks. The initial release was way back in 2001 and it has grown to provide a host of features. The list includes low-level tasks such as tokenization (it provides different tokenizers), n-gram analysers, collocation parsers, POS taggers, NER and many more. SourceNLTK is primarily for English based NLP tasks. It utilizes years of research into linguistics and machine learning to provide such features. It is widely used in academic and industrial institutions across the world. Official LinkAdvantagesThe goto library for most NLP related tasksProvides a huge array of algorithms and utilities to handle NLP tasks, right from low-level parsing utilities to high-level algorithms like CRFsExtensible interface which allows us to train and even extend existing functions and algorithmsDownsidesMostly written in java, it has overheads and limitations in terms of the amount of memory required to handle huge datasetsInability to interface with the latest advancements in NLP using deep learning models22.  GensimGensim is a fast and production ready NLP library. It is particularly designed for unsupervised topic modeling tasks apart from the usual set of NLP tasks. Out of the box, it provides algorithms such as Latent Semantic Analysis (LSA, LSI), matrix based (SVD, NMF) and Latent Dirichlet Allocation or LDA. It also provides functionalities for generating word representations using fastText and word2vec (and their variants).Word2Vec based similarity using GensimGensim also has capabilities to handle large volumes of text using streaming and out of memory implementation of various algorithms. This capability along with robustness and efficient implementations set it apart from other NLP libraries. Official Link23.  SpacySpacy is a Natural Language Processing library designed for multiple languages like English, German, Portuguese, French, etc. It has tokenizers and NER (Named Entity Recognizers) for various languages. Unlike NLTK which is widely used for academic purposes, spacy is designed to be production ready.Dependency Parser and its Visualization using Spacy.Apart from providing traditional NLP capabilities, spacy also exposes deep learning based approaches. This enables it to be easily used with frameworks like Tensorflow, keras, Scikit-learn and so on. It also provides pre-trained word vectors in various languages. Explosion AI, the company behind spacy has also developed various extensions to enhance its capabilities by providing visualizations(displayCy), machine learning algorithms(Thinc), etc. Official LinkVisualization24.  SeabornBuilt on top of matplotlib, seaborn is a high-level visualization library. It provides sophisticated styles straight out of the box (which would take some good amount of effort if done using matplotlib).Sample plots using seaborn.Apart from styling prowess and sophisticated color pallets, seaborn provides a range of visualizations and capabilities to work with multivariate analysis. It provides capabilities to perform regression analysis, handling of categorical variables and aggregate statistics. Official Link25.  BokehBokeh is short for visualization on steroids! No, this isn’t a joke. Bokeh provides interactive zoomable visualizations using the power of javascript in a python environment. Bokeh Visualizations are a perfect solution for sharing results through a Jupyter notebook along with its interactive visualizations.SourceIt provides two modes of operation. A high-level mode for easily generating complex plots. It also has a low-level mode which provides much more controls for customizations. It is useful for preparing dashboards and other data related applications which are used through browsers. Official linkAdvantagesCapability to generate interactive visualizations with features like hover text, zoom, filter, select, etcAesthetically superior visualizationslow-level and high-level modes to support high flexibility and rapid prototypingDisadvantagesInability to be packaged with saved state with jupyter notebooksThe interface is slightly different than other visualization libraries, thus making it difficult to migrate from one library to another.26.  PlotlyPlotly is a production grade visualization platform with wrappers in not just python but other languages like R, Julia, MATLAB, etc. Plotly provides visualizations, online plotting, statistical tools along with a suite of solutions like Dash and Chart Studio to cater to different needs.SourcePlotly also provides capabilities to convert matplotlib and ggplot visualizations into interactive ones. It is extensively used by some of the industry leaders. Unlike most libraries discussed so far, plotly has commercial offerings as well. Official LinkMiscellaneousSo far we have discussed the most important, popular and widely used python libraries which are essential for different tasks within the Machine Learning workflow. There are a few more which are also used in the same workflows (or depending upon the use case/scenario). These might not be directly helping you build ML models/algorithms but these are nonetheless important in the overall lifecycle. Let us look at a few of them:IPython and JupyterIPython or Interactive Python is a command line interface, originally developed for python (now supports multiple languages). It supports parallel computing and a host of GUI toolkits. It also forms the core of web application based notebook server called Jupyter. Jupyter is a loose acronym for Julia Python and R (thought now it supports more languages). It allows us to prepare and share documents which contain live code, interactive visualizations, markdown and slideshow capabilities.IPython and Jupyter are the two most widely used shells/applications by Data Scientists to share their work and develop models.Official Links:IPythonJupyter27.  ScrapyWeb or the internet is an immense source of data. Scrapy is one of the leading libraries utilized to scrape websites or build spiders/crawlers to do the same. It now also supports connecting to APIs to get data. Official LinkBeautifulSoupOnce you have the scraped text, the next requirement is the capability to extract information from HTML and XML data. Beautifulsoup is a library with capability to parse HTML and XML documents. It does so by generating parse trees from such documents. The documentation for Beautifulsoup is very nicely done and acts as a primer for most requirements. Official Link29. FlaskFlask is a lightweight microframework for web in python. It is as bare bones a framework as possible to get up and running with a webserver/application. It supports extensions which enhance its capabilities to the full. Flask is based on Werkzeug (a Web Server Gateway Interface/WSGI) and Jinja 2 (a templating engine).Hello World Example to get started with Flask.Flask is used across the board, even some of the big industry players like LinkedIn. Official Link30.  OpenCVOpen Source Computer Vision or OpenCV for short is a computer vision library for python. It provides a huge list of computer vision related capabilities for handling 2D and 3D data. It is an actively developed project with cross-platform capabilities. It works well with deep learning frameworks like Tensorflow, PyTorch, etc. Official LinkBonus: Few libraries/repositories which are quite widely used.The python ecosystem is abuzz with new and exciting stuff every day. Researchers and developers are working to bring forward their work to improve your workflows and enhance the python ecosystem as well. The following is a quick list of more such work, some of which are yet available only as GitHub repositories:scikit-learn-contribThis is a collection of high quality scikit-learn compatible projects. Some of the projects from this collection include imbalanced-learn, lightning, hdbscan, etc. OfficiallinkDaskDask is parallel computing python library. It works/integrates easily with existing libraries like pandas and numpy. It provides pandas like interface with the power of parallel computing. Official linkkeras_experimentsThis github repository further enhances the capabilities of keras. It exposes experimental work based on keras APIs. The primary goal is to provide capability to utilize multiple GPUs. Official Linkdata.tableThis library provides capability to work with and manipulate tabular data structures. The aim is to be analogous to R SFrames. The functionalities are similar to pandas (or restricted) and focus is towards big data. Official LinkPython Build Systempip and conda are two amazing package managers in the python ecosystem. For our understanding in this article, it suffices to know that these two package managers are what allow us to setup the required libraries.SourceThe build system of python is a love-hate relationship. It is easy to use for most tasks, yet it can be mind-boggling to figure out setups for some of the most widely used libraries (say numpy, matplotlib). The tasks get slightly more complicated when you are working on an OS which has system-installed version of python. Proceed with caution and read installation steps before installing the libraries.ConclusionSourceThis article began by providing you the motivations and possible reasons behind python being the go-to choice for Machine Learning tasks. The python ecosystem is huge, both in terms of contribution and usage. We discussed about libraries used in all major areas of Machine Learning, right from data manipulation stage to deep learning, natural language processing and even visualization. Python has a diverse set of libraries available which not only enhance its capabilities but also showcase the breadth and depth of tasks one can perform. There are de-facto standards for most tasks (like scikit-learn, tensorflow, etc.) yet there is no dearth of alternatives. In the end we briefly discussed about the python build system and the issues associated with it. Through this article we have tried to provide you with an extensive list of libraries, yet this is by no means an exhaustive list. There are many more amazing libraries being used and worked upon. If you know any such, do share in the comments below.
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Top 25 Python Libraries for Machine Learning

Top 25 Python Libraries for Machine Learning

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Python and Its EcosystemPython is one of the most widely used languages by Data Scientists and Machine Learning experts across the world. Though there is no shortage of alternatives in the form of lan...
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Docker Vs Virtual Machine: Understand the differences

Virtual machines and Docker containers, both are more than enough in order to get the most out of computer resources available in hardware and software. Docker containers are kind of new on the block, but virtual machines or VMs have been there and will continue to remain popular in data centres of all sizes. If you are looking for the best solution to run your services in the cloud, it is advised that you understand these virtualization technologies first. Learn about the differences between the two, the best way they can be used, and the capabilities each one possesses.Most of the organizations have either moved or are planning to move from on-premise computing services to cloud computing services. Cloud computing allows you access to a large pool of configurable resources that can be shared, for example - computer networks, servers, storage, applications, and services. For the implementation of cloud computing in a traditional way, virtual machines are used. However, these days Docker containers have gained a lot of popularity due to its features, as well as Dockers are considered to be of a lightweight compared to virtual machines which are heavier.According to reports, there will be a rise in the use of application containers of 40% by the end of the year 2020. Docker containers have gained a lot of popularity as it facilitates rapid and agile development. But the question arises - How are Docker containers different from virtual machines? The most important thing to know is that Docker containers are not virtual machines or lightweight virtual machines or trimmed down virtual machines. Let us compare the two and understand the major differences.What is exactly a Virtual Machine?It is said that Virtual machines were born when server processing power and capacity was increased but bare metal applications were unable to exploit the new abundance in resources. Virtual machines were built by running software on top of physical servers in order to match the requirements of a particular hardware system. A virtual machine monitor or the hypervisor is a firmware, software or hardware which helps in creating a virtual machine and runs it. It is a necessary component to virtualize the server and it sits between the virtual machine and the hardware. As cloud computing services are available and virtualization is affordable, a lot of large as well as small IT departments have adapted virtual machines in order to reduce costs and increase efficiency.Understanding Virtual MachinesLet us understand how virtual machines work starting from the bottom-most layer:Infrastructure: This can be anything, your PC or laptop, a dedicated server running in a data centre, a private virtual server used in the cloud such as Amazon EC2 instance.Host Operating System: Just on top of the infrastructure layer lies the host which runs an operating system. While you use your laptop, it will likely be Windows, MacOS or Linux. As we are discussing virtual machines, it is commonly labelled as the host operating system.Hypervisor: It is also called a virtual machine monitor. You can consider a virtual machine as a self-contained computer packed into a single file, but something is required to be able to run the file. Type 1 hypervisors and Type 2 hypervisors are used to do so. In Type 1 hypervisor, Hyper-V for Windows, HyperKit for MacOS and KVM for Linux. Some popular Type 2 hypervisors are VirtualBox and VMWare.Guest Operating System: Suppose you would like to run three applications on your server under total isolation. To run, you will need 3 guest operating systems. These guest operating systems are controlled by the hypervisors. Each guest operating system takes a disk space of around 700 MB, so the total of disk space that you use is 2.1GB utilized by guest OS and it gets more complicated when guest OS uses its own CPU and memory resources as well. This is what makes the virtual machine heavy.BINS/LIBS: Each guest operating system uses its own set of various binaries and libraries in order to run several applications. For example, if you are using Python or Node JS you will have to install packages accordingly from this layer. Since each application will be different than the other, it is expected that each application will have its own set of library requirements.Application Layer: This is the layer where you have your source code for the magical application you have developed. If you want each of these applications to be isolated, you will have to run each application inside its own guest operating system.Types of Virtual MachinesThere are different types of virtual machines, each offering various functions:System Virtual MachinesA system virtual machine is a virtual machine which allows multiple instances of the operating system to run on a host system and share the physical resources. They emulate an existing architecture and are built with the purpose of providing a platform to run several programs where real hardware is not available for use. Some of the advantages of system virtual machines are -Multiple OS environments can accommodate the same primary hard drive with a virtual partition which allows sharing files generated in either the “guest” virtual environment or the “host” operating system.Application provisioning, high availability, maintenance and disaster recovery are inherent in the virtual machine software selected.Some of the disadvantages of system virtual machines are mentioned below:When a virtual machine accesses the host drive indirectly, it becomes less efficient than the actual machine.Malware protection for virtual machines are not very compatible with the "host" and sometimes require separate software.Process Virtual MachinesA process virtual machine is also known as an application virtual machine, or Managed Runtime Environment (MRE). It is used to execute a computer program inside a host OS and it supports a single process. A process virtual machine is created when the process starts and is destroyed as soon as you exit the process. The main purpose of this type of virtual machine is to provide a platform-independent programming environment.Benefits of Virtual MachinesVirtualization provides you with a number of advantages such as centralized network management, reducing dependency on additional hardware and software, etc. Apart from these, virtual machines offer a few more benefits:Multiple OS environments can be used simultaneously on the same machine, although isolated from each other.Virtual machines have the ability to offer an instruction set architecture which differs from real computersIt has easy maintenance, application provisioning, availability and convenient recovery.Popular VM ProvidersHere are some of the selected software we think is best suited for people who want to keep things real, virtually.Oracle VM VirtualboxOracle VM Virtualbox is free of cost, supports Windows, Mac and Linux, and it has the ability to host for 100,000 registered users. If you are not sure about which operating system you should choose to use, Oracle VM VirtualBox is a really good choice to go ahead with. It supports a wide range of host and client combinations. It supports operating systems from Windows XP onward, any Linus level above 2.4, Solaris, Open Solaris and even OpenBSD Unix. It also runs on Apple’s MacOS and can host a client Mac VM session.VMware Fusion and WorkstationVMware Workstation and VMware Fusion are the industry leaders in virtualization. It is one of the few hosts which support DirectX 10 and OpenGL 3.3. It also supports CAD and other GPU accelerated applications to work under virtualization.Red Hat VirtualizationRed Hat Virtualization has more of enterprise users with powerful bare-metal options. It has two versions: a basic version which is included in Enterprise Linux with four distinct VMs on a single host and the other one is a more sophisticated Red Hat virtualization edition.Important features of virtual machinesA typical virtual machine has the following hardware features.The hardware configuration of the virtual machine is similar to that of the default hardware configuration settings.There is one processor and one processor per core. The execution mode is selected for the virtualization engine based on the host CPU and the guest operating system.A single IDE CD/DVD drive is available which is configured after receiving power and detects automatically as a physical drive on the host system when connected.A virtual network adapter is used which gets configured upon power on and uses network address translation (NAT). With the help of NAT networking, virtual machines are able to share the IP address of the host system.It has one USB controller.It has a sound card configured to use the default sound card on the host system.It has one display configured to use the display settings on the host computer.Some of the software features include:The virtual machine is not encrypted.Drag-and-drop, cut and paste features are available.Remote access by VNC clients and shared folders are disabled.What are Containers?A container is a standard unit of software which packages up the code and all its dependencies in order to run the application reliably and quickly from one computing environment to another. A Docker container image is a standalone, lightweight, executable package of the software which includes everything needed to run an application such as system tools and libraries, code, runtime, and settings.Understanding Docker ContainerThere is a lot less baggage compared to virtual machines. Let us understand each layer starting from the bottom most.Infrastructure: Similar to virtual machines, the infrastructure used in Docker containers can be your laptop or a server in the cloud.Host Operating System: This can be anything which is capable of running Docker. You can run Docker on MacOS, Windows and Linux.Docker Daemon: It is the replacement for the hypervisor. Docker Daemon is a service which runs in the background of the host operating system. It also manages the execution and interaction with Docker containersBINS/LIBS: It is similar to that on virtual machines except it is not running on a guest operating system, instead special packages called Docker images are built and finally the Docker daemon runs the images.Application: This is the ultimate destination for the docker images. They are independently managed here. Each application gets packed with its library dependencies into the same Docker image and is still isolated.Types of ContainerLinux Containers (LXC) — LXC is the original Linux container technology. It is a Linux operating system level virtualization method which is used to run multiple isolated Linux systems on a single host.Docker — Docker was first started as a project in order to build single-application LXC containers. This makes the containers more flexible and portable to use. Docker acts a Linux utility at a higher level and can efficiently create, ship, and run containers.Benefits of ContainersIt reduces IT management resourcesIt reduces the size of snapshotsIt reduces and simplifies security updatesNeeds less code in order to migrate, transfer, and upload workloadsPopular Container ProvidersLinux ContainersLXCLXDCGManagerDockerWindows Server ContainersDocker vs Virtual Machines How is a Docker Container different from a Virtual Machine?Containers are user space of the operating system whereas Docker is a container based technology. Dockers are built for running various applications. In Docker, the containers running share the host Operating system kernel.Virtual machines are not based on container technology. They are mainly made up of kernel space along with user space of an operating system. The server's hardware is virtualized and each virtual machine has operating systems and apps which shares hardware resources from the host.Both virtual machines and dockers come with merits and demerits. Within a container environment, multiple workloads can run with one operating system. It also results in reduced IT management resources, reduces the size of snapshots, quicker spinning up apps, less code to transfer, simplified and reduced updates and so on. However, within a virtual machine environment, each workload needs a complete operating system.Basic Differences between Virtual Machines and ContainersVirtual MachinesContainersVMs are heavyweightContainers are lightweightIt has limited performanceIt has native performanceEach of the virtual machines runs in its own Operating SystemAll containers share the host operating systemIt has hardware-level virtualizationIt has OS virtualizationIt takes minutes to startupIt takes milliseconds to startupRequired memory is allocatedIt requires very less memory spaceAs it is fully isolated and hence it is more secureProcess-level isolation takes place in containers, thus less secure compared to VMsUses for VMs vs Uses for ContainersBoth containers and VMs have benefits and drawbacks, and the ultimate decision will depend on your specific needs, but there are some general rules of thumb.VMs are a better choice for running apps that require all of the operating system’s resources and functionality when you need to run multiple applications on servers or have a wide variety of operating systems to manage.Containers are a better choice when your biggest priority is maximizing the number of applications running on a minimal number of servers.Who wins amongst the two?When To Use a Container vs. When to Use a Virtual MachineContainers and virtual machines, each thrive in different use cases. Let us check some of the cases and know when to use a container and when is it a good choice to use virtual machines.Virtual machines take a good amount of time to boot and shut down: This feature is heavily used in development and testing environments. If you have to spin up and power down machines regularly or clone machines, Docker containers are what you should choose over virtual machines.Containers are geared based on Linux: Virtual machines are a better choice when you want to virtualize another operating system.Dockers do not have many automation and security features: Most of the fully fledged virtual management platforms provide a variety of automation features along with built-in security from kernel level to network switches.Virtual Machine and Container Use CasesThere is a fundamental difference between the usage of containers and virtual machines. Virtual machines are applicable for virtual environments, whereas containers use the underlying operations system and do not require a hypervisor.Let us see some use cases:Virtualized EnvironmentsIn a virtualized environment, multiple operating systems are run on a hypervisor which manages the I/O on one particular machine. However, in a containerized environment, it is not virtualized and hypervisor is not used. That does not mean you cannot run a container in a virtual machine.You can run containers in a virtual machine. We know containers run on a single Operating System. As it can run several containers on one physical system, it is like mini-virtualization without a hypervisor. Hypervisors face certain limitations related to performance and it also blocks certain server components like networking controller.DevOpsContainers are used in the DevOps environment for their develop-test-build. These containers perform much faster than virtual machines, they have faster spun up and down and have better access to system resources.Containers are smaller in size and have the ability to run several servers and hundreds of virtual machines. This shows that containers have greater modularity over virtual machines. Using microservices, an app can be split into multiple containers. Due to this combination, you can avoid potential crashes and this will also help you isolate problems.Older SystemsVirtual machines are capable of hosting an older version of an operating system. Suppose an application was built for an operating system many years back, which is quite unlikely to run in a newer generation operating system. In such cases, you can run the old operating system in a virtual machine and without any changes in the app you can run it.More Secure EnvironmentsAs container needs frequent interaction with the underlying operating system or other containers, there is a security risk associated. However, in comparison to containers, virtual machines are ideal and considered to be a more secure environment.
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Docker Vs Virtual Machine: Understand the differences

Docker Vs Virtual Machine: Understand the differences

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Virtual machines and Docker containers, both are more than enough in order to get the most out of computer resources available in hardware and software. Docker containers are kind of new on the block,...
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Python in a Nutshell: Everything That You Need to Know

Python is one of the best known high-level programming languages in the world, like Java. It’s steadily gaining traction among programmers because it’s easy to integrate with other technologies and offers more stability and higher coding productivity, especially when it comes to mass projects with volatile requirements. If you’re considering learning an object-oriented programming language, consider starting with Python.A Brief Background On Python It was first created in 1991 by Guido Van Rossum, who eventually wants Python to be as understandable and clear as English. It’s open source, so anyone can contribute to, and learn from it. Aside from supporting object-oriented programming and imperative and functional programming, it also made a strong case for readable code. Python is hence, a multi-paradigm high-level programming language that is also structure supportive and offers meta-programming and logic-programming as well as ‘magic methods’.More Features Of PythonReadability is a key factor in Python, limiting code blocks by using white space instead, for a clearer, less crowded appearancePython uses white space to communicate the beginning and end of blocks of code, as well as ‘duck typing’ or strong typingPrograms are small and run quickerPython requires less code to create a program but is slow in executionRelative to Java, it’s easier to read and understand. It’s also more user-friendly and has a more intuitive coding styleIt compiles native bytecodeWhat It’s Used For, And By WhomUnsurprisingly, Python is now one of the top five most popular programming languages in the world. It’s helping professionals solve an array of technical, as well as business problems. For example, every day in the USA, over 36,000 weather forecasts are issued in more than 800 regions and cities. These forecasts are put in a database, compared to actual conditions encountered location-wise, and the results are then tabulated to improve the forecast models, the next time around. The programming language allowing them to collect, analyze, and report this data? Python!40% of data scientists in a survey taken by industry analyst O’Reilly in 2013, reported using Python in their day-to-day workCompanies like Google, NASA, and CERN use Python for a gamut of programming purposes, including data scienceIt’s also used by Wikipedia, Google, and Yahoo!, among many othersYouTube, Instagram, Quora, and Dropbox are among the many apps we use every day, that use PythonPython has been used by digital special effects house ILM, who has worked on the Star Wars and Marvel filmsIt’s often used as a ‘scripting language’ for web apps and can automate a specific progression of tasks, making it more efficient. That’s why it is used in the development of software applications, web pages, operating systems shells, and games. It’s also used in scientific and mathematical computing, as well as AI projects, 3D modelers and animation packages.Is Python For You? Programming students find it relatively easy to pick up Python. It has an ever-expanding list of applications and is one of the hottest languages in the ICT world. Its functions can be executed with simpler commands and much less text than most other programming languages. That could explain its popularity amongst developers and coding students.If you’re a professional or a student who wants to pursue a career in programming, web or app development, then you will definitely benefit from a Python training course. It would help if you have prior knowledge of basic programming concepts and object-oriented concepts. To help you understand how to approach Python better, let’s break up the learning process into three modules:Elementary PythonThis is where you’ll learn syntax, keywords, loops data types, classes, exception handling, and functions.Advanced PythonIn Advanced Python, you’ll learn multi-threading, database programming (MySQL/ MongoDB), synchronization techniques and socket programming.Professional PythonProfessional Python involves knowing concepts like image processing, data analytics and the requisite libraries and packages, all of which are highly sophisticated and valued technologies.With a firm resolve and determination, you can definitely get certified with Python course!Some Tips To Keep In Mind While Learning PythonFocus on grasping the fundamentals, such as object-oriented programming, variables, and control flow structuresLearn to unit test Python applications and try out its strong integration and text processing capabilitiesPractice using Python’s object-oriented design and extensive support libraries and community to deliver projects and packages. Assignments aren’t necessarily restricted to the four-function calendar and check balancing programs. By using the Python library, programming students can work on realistic applications as they learn the fundamentals of coding and code reuse.
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Python in a Nutshell: Everything That You Need to Know

Python in a Nutshell: Everything That You Need to Know

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Python is one of the best known high-level programming languages in the world, like Java. It’s steadily gaining traction among programmers because it’s easy to integrate with other technol...
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Top 5 Benefits of Data Science With Python Foundation Training

There are vast amounts of data generated every second. From your smartphone to your online behavior, every action you take can be consolidated into big data. Now, this huge amount of data houses great potential. Businesses and corporations can use this data to understand user behavior, predict patterns, and be better prepared to deal with future challenges. To do this, they need data scientists.Data science is a relatively new term that has taken the world by storm. The profession has been named the “Sexiest Job of the 21st Century” by Harvard Business Review. This job offers many perks and is being touted as the most in-demand profession. Data scientists are all-rounders who need to know at least the basics of statistics, maths, and computer science.With time, Python has become a popular choice among data scientists and getting a data science with Python foundation training can do wonders for your career.Why Data Scientist’s are in high demand?Is becoming a data scientist all that it is made up to be? Is it really worth the effort? Why should I choose it as my career? These are valid questions that anyone can have. Here are a few reasons that justify the choice of data science as a career.Demand Across IndustriesSince data science is related to computer science and requires coding skills, many think that a data scientist is a tech industry job. This is not entirely true. Almost every industry has a need for data scientists, from tech to gaming to the financial sector to retail. You can pick the industry that you want to work in and become a data scientist in that industry.Shortage of Data ScientistsAs per a report by IBM, there will be a shortage of about 62,000 data scientists by the year 2020. This can also be attested by the fact that around 80% of people working in the field right now say that there is a severe shortage of trained data scientists right now.High SalarySince there is a wide disparity between the demand and supply of data scientists, the job currently fetches a high salary. This is the most in-demand profession right now and a well-trained data scientist with good qualifications can easily get an impressive package. The average salary of a data scientist is the US right now is around USD 91,000 per annum.Average Company wise Data Scientist SalaryExciting ChallengesWhen you work in a new field that is still in its infancy such as data science, the potential for learning on the job is enormous. You will be constantly discovering new things and finding new solutions to problems or facing new challenges. If you like a challenge and want to constantly reinvent yourself and change your thinking, then you should definitely consider becoming a data scientist.Immense GrowthThe field of data science has a projected growth of around 11% between 2014 and 2024. This means that the demand for data scientists with Python foundation training is set to increase. The field is also growing faster than any of its counterparts.Why is Python Foundation Training Important for a Data Scientist?The language used by a data scientist can have a great impact on the time taken to analyze the data and interpretation of the results. Python is one of the most popular languages used by data scientists. Its simplicity, scalability, flexibility, and power are the main reasons for this.Python is relatively easy to learn. Even a non-programmer can understand the basics and start coding. Python also has good community support. If you ever get stuck while learning and need to clear some doubts, just post your query online and your doubt will be cleared in no time. The Python community is also actively involved in building new packages that are helpful for data scientists. This has only made the language more attractive to data scientists and has increased its adoption in the field.Not only is Python a powerful language that does quite a lot with just a few lines of code, but it is also well-supported by powerful packages that make it easier to solve complex data science problems.Give Your Career a Boost!Data science with Python foundation course can help your career reach great heights. It is the best way to enter an exciting profession whose impact can be felt globally in our everyday life. It is also a good way to enter a high paying career where you also get to keep learning.
Rated 4.5/5 based on 19 customer reviews
Top 5 Benefits of Data Science With Python Foundation Training

Top 5 Benefits of Data Science With Python Foundation Training

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There are vast amounts of data generated every second. From your smartphone to your online behavior, every action you take can be consolidated into big data. Now, this huge amount of data houses great...
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The Ultimate Guide to Node.Js

IT professionals have always been in much demand, but with a Node.js course under your belt, you will be more sought after than the average developer. In fact, recruiters look at Node js as a major recruitment criterion these days.  Why are Node.js developers so sought-after, you may ask. It is because Node.js requires much less development time and fewer servers, and provides unparalleled scalability.In fact, LinkedIn uses it as it has substantially decreased the development time. Netflix uses it because Node.js has improved the application’s load time by 70%. Even PayPal, IBM, eBay, Microsoft, and Uber use it. These days, a lot of start-ups, too, have jumped on the bandwagon in including Node.js as part of their technology stack.The Course In BriefWith a Nodejs course, you learn beyond creating a simple HTML page, learn how to create a full-fledged web application, set up a web server, and interact with a database and much more, so much so that you can become a full stack developer in the shortest possible time and draw a handsome salary. The course of Node.js would provide you a much-needed jumpstart for your career.Node js: What is it?Developed by Ryan Dahl in 2009, Node.js is an open source and a cross-platform runtime environment that can be used for developing server-side and networking applications.Built on Chrome's JavaScript runtime (V8 JavaScript engine) for easy building of fast and scalable network applications, Node.js uses an event-driven, non-blocking I/O model, making it lightweight and efficient, as well as well-suited for data-intensive real-time applications that run across distributed devices.Node.js applications are written in JavaScript and can be run within the Node.js runtime on different platforms – Mac OS X, Microsoft Windows, Unix, and Linux.What Makes Node js so Great?I/O is Asynchronous and Event-Driven: APIs of Node.js library are all asynchronous, i.e., non-blocking. It simply means that unlike PHP or ASP, a Node.js-based server never waits for an API to return data. The server moves on to the next API after calling it. The Node.js has a notification mechanism (Event mechanism) that helps the server get a response from the previous API call.Superfast: Owing to the above reason as well as the fact that it is built on Google Chrome's V8 JavaScript Engine, Node JavaScript library is very fast in code execution.Single Threaded yet Highly Scalable: Node.js uses a single threaded model with event looping, in which the same program can ensure service to a much larger number of requests than the usual servers like Apache HTTP Server. Its Event mechanism helps the server to respond promptly in a non-blocking way, eliminating the waiting time. This makes the server highly scalable, unlike traditional servers that create limited threads to handle requests.No buffering: Node substantially reduces the total processing time of uploading audio and video files. Its applications never buffer any data; instead, they output the data in chunks.Open source: Node JavaScript has an open source community that has produced many excellent modules to add additional capabilities to Node.js applications.License: It was released under the MIT license.Eligibility to attend Node js CourseThe basic eligibility for pursuing Node training is a Bachelors in Computer Science, Bachelors of Technology in Computer Science and Engineering or an equivalent course.As prerequisites, you would require intermediate JavaScript skills and the basics of server-side development.CertificationThere are quite a few certification courses in Node Js. But first, ask yourself:Do you wish to launch your own Node applications or work as a Node developer?Do you want to learn modern server-side web development and apply it on apps /APIs?Do you want to use Node.js to create robust and scalable back-end applications?Do you aspire to build a career in back-end web application development?If you do, you’ve come to the right place!Course CurriculumA course in Node JavaScript surely includes theoretical lessons; but prominence is given to case studies, practical classes, including projects.  A good certification course would ideally train you to work with shrink-wrap to lock the node modules, build a HTTP Server with Node JS using HTTP APIs, as well as about important concepts of Node js like asynchronous programming, file systems, buffers, streams, events, socket.io, chat apps, and also Express.js, which is a flexible, yet powerful web application framework.Have You Decided Yet? Now that you know everything there is to know about why you should pursue a Node js course and a bit about the course itself, it is time for you to decide whether you are ready to embark on a journey full of exciting technological advancements and power to create fast, scalable and lightweight network applications.
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The Ultimate Guide to Node.Js

The Ultimate Guide to Node.Js

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IT professionals have always been in much demand, but with a Node.js course under your belt, you will be more sought after than the average developer. In fact, recruiters look at Node js as a major re...
Continue reading

Top 6 Benefits of Learning Data Science with Python

Over the last decade, a new requirement has emerged in the industry that has taken the world by storm and has completely revamped our thinking. This requirement is none other than that of Data Scientists. Data Scientist is one of the hottest requirements in the job market. One of the main reasons for this widespread popularity is that data analytics can find use in all industries. Its use is not limited to just the software or IT industry. It has found application in industries such as intelligence and security, healthcare, business, government, energy, and much more. This article will not only give you reasons on why you need to learn data science, but it will also tell you why learning data science with Python training is the better option.Why Learn Data Science?Data analytics is all about solving problems. It involves looking at the data you have and using it to solve a problem that you are either facing currently or you anticipate you will have to face in the future. One of the main advantages of studying data science is that you can work in the field you like. Every industry has its own unique set of present and future problems and data science is the way to solve them. This is why every industry is currently looking for data scientists and you can have your pick among them. You will not get this option with any other course.Data Science is not just the current trend, it is also the future. When you are planning your career, it is important to consider the present as well as future requirements. Currently, there is a shortage of data scientists. Companies are looking to hire more people in this post but they are unable to find qualified candidates. Studying data science or data analytics right now will put you on the path of some very lucrative career choices.Why learn Data Science with Python Training?While there are many different ways to implement data analytics, Python has become very popular and rightfully so. Python is a powerful language that is easy to learn and implement. Here is why you should learn data science with Python training.1. Ease of LearningPython is one of the easiest languages to learn. Even if you have no background with coding, learning Python will not be difficult. One of the main things that hold people back when they hear about becoming a data scientist is the lack of coding skills and the perceived difficulty in learning the same. You won’t face this problem with Python.2. Faster Development and ProcessingWhile dealing with huge amounts of data, speed is key. A slow language can slow things down incredibly. Python is a clean, easy to handle language that requires only a few lines of coding. This significantly cuts down on the coding time required. Python’s slow execution was one of the reasons that held it back from being fully accepted. However, since the introduction of the Anaconda platform, even this complaint has been dealt with.3. Powerful PackagesPython also comes with huge range packages such as NumPy, SciPy, PyBrain, Pandas, etc. that makes it incredibly simple to code complex data analytics problems. There are also many libraries that support the integration of Python with other languages such as C and SQL. These further aid Python in making it more powerful.4. Community SupportOne thing that makes Python is easy to learn and understand is its strong community. Any time you get stuck with any problem, you can ask the community and they will always help you. In addition to this, many in the community are also constantly developing new packages and libraries for a variety of uses. With the popularity of Python for data science increasing, many of these are being developed for the use of data scientists.5. Better Data VisualisationVisualization is key for data scientists as it helps them understand the data better. With libraries such as ggplot, Matplotlib, NetworkX, etc. and APIs such as Plotly, Python can help you create stunning visualizations. You can also integrate other big data visualization tools in Python. All of this adds to Python’s usefulness for a data scientist.6. Compatible with HadoopOne of the most popular open source platforms for big data, Hadoop is inherently compatible with Python. The Python package known as PyDoop lets you access the API for Hadoop. This lets you write Hadoop programs using Python. The package also lets you write code for complex problem solving with little effort.Kickstart Your CareerIf you are at the start of your professional journey and are thinking about which path to take, then you should definitely consider going for data science with Python course. This is one of the most sought after career options that can set you on the fast track for a very high paying and exciting profession.
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Top 6 Benefits of Learning Data Science with Python

Top 6 Benefits of Learning Data Science with Python

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Over the last decade, a new requirement has emerged in the industry that has taken the world by storm and has completely revamped our thinking. This requirement is none other than that of Data Scienti...
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Top 8 Advantages Of Learning React JS

A compelling Frontend web development tool, React JS allows the building of rich, interactive user interface (UI) for single page web applications with relative ease. This open-source JavaScript library handles view layers of mobile and web apps. The appeal of React JS also lies in the creation of UI components which can be used across multiple pages.Jordan Walke, Facebook’s software engineering team’s member, had developed React JS for deploying components on social media’s newsfeed way back in 2011. Instagram started leveraging its potential in 2012.Why do you need to learn React JS?React JS is critical for creating large web apps for your organization in an agile manner. Data can dynamically get updated in web pages created by you without requiring them to be reloaded at each update trigger. Application UIs can be designed in a scalable, swift, and easy manner. Within the MVC (Model View Controller) template, you can combine the usage of React JS with other frameworks and libraries of JavaScript like AngularJS.Advantages of Learning React JSSubscribing to React JS course would allow you to become more proficient in leveraging the tool for creating engaging and responsive UIs. Some of the incentives for learning the tool are discussed below.1. Easy to LearnThis library is lightweight and concerns itself with the application’s view layer only. Just understanding the basics would get you started with building useful web apps.2. Components Are ReusableThe tool has a component driven structure. Dropdown, checkbox, and other such smaller components form the building block within a wrapper component. Next, wrapper components of higher level have to be written. This process is iterated until the final root component or app is in place.The internal logic and rendering philosophy of each component is unique. By reusing them, you can ensure consistent appearance for your app and keep adding to your codebase. Large apps can be easily built by structuring the components strategically.3. Optimum Performance with Virtual DOMOften, performance issues are common in web UIs which experience optimal user interaction and need frequent view updates. React JS surmounts bottlenecks by using virtual DOM (Document Object Model) which is maintained in memory. Changes in view are communicated to virtual DOM first which triggers a differential algorithm for comparing the present and earlier states of virtual DOM. It also calculates the leanest possible manner to apply changes with lesser number of updates. The updates are then passed on to DOM to reflect changes in the lowest write time.   4. Good AbstractionAs a user, you would not be exposed to complex internal mechanisms like digest cycles. For building forms, you need to learn the life cycles of components, props, and states. Your productivity in designing architecture of app increases as you need not compulsorily learn patterns like MVVM.5. Complemented by Flux ArchitectureFlux, Facebook’s web app building architecture, facilitates optimization of React JS with a unidirectional flow of data. The helper methods, action creators, are collected within a library. Action is created from method parameters which are then assigned a type and sent to the dispatcher. The dispatcher forwards each action to stores with callbacks that were used by stores for registering with a dispatcher.A change event is generated once updating of stores occur in response to a particular action. Change events are listened to by controller views which allow retrieval of fresh data from stores for providing the same to complete tree of own child views. The central theme is that actions are created which are coordinated by the main dispatcher for updating stores which subsequently update the views.Data meant for display by components are preserved in stores which syncs the data across the application consistently.6. JSX for TemplatingJSX is simplified JavaScript that facilitates HTML quoting. The syntaxes of HTML tags are used for rendering subcomponents.7. Awesome Developer ToolsReact Developer tools can be availed of as Chrome extension. The tools allow inspection of hierarchy embedded React components and viewing corresponding states and props.8. React NativeReact Native allows creation of native apps for mobile OS like Android. The code for the webpage cannot be used unaltered in React Native, but the same architecture and methodology can be used. You can learn more about its potential during React JS training.How to Become Proficient in React JS?React JS Training is the best way to become aware of the functionalities of this cutting-edge tool. React JS course in instructor-led training classes empowers you to develop rich internet applications by integrating JSX, state management, routing, master components, hooks, etc. Certification from a leading knowledge center would build your hands-on skills by offering scope for working on live projects and assignments under discerning mentors.Complete hands-on React JS training would make you conversant with a complete array of new features available in recent React 16.8 version. You will learn ways to build React development environment from scratch using a web pack and troubleshoot errors using VSCode debugger.The Way ForwardTop websites like Yahoo, BBC, Paypal, etc. are the leaders among 1.06 million websites that use React JS. In India, React JS library is the 10th most popular among developers. In June 2018, React JS had found mention in more than 28% of job postings in the most popular web development frameworks’ category worldwide. Very soon, it is expected to overtake Angular JS in terms of popularity and job demand.
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Top 8 Advantages Of Learning React JS

Top 8 Advantages Of Learning React JS

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A compelling Frontend web development tool, React JS allows the building of rich, interactive user interface (UI) for single page web applications with relative ease. This open-source JavaScript libra...
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MEAN Stack Web Development: A Beginner’s Guide

A stack is all the programming languages, frameworks, libraries, tools, etc. used in software development of a particular project. MEAN stack is a combination environment with frameworks and a database which is completely sufficient for end-to-end functional web development. You will only need peripherals like analytical tools and a hosting platform, if necessary, to make your website market ready. MEAN Stack Web Development training is essential for every front-end developer, as it gives you the flexibility to understand other programming languages.What Does MEAN Stand For?M - MongoDB: Any website needs a database to store information about what to show on the website, as well as information provided by the users, and how the users have interacted with the website. There are two types of database popularly used by most developers; which are the relational and non-relational database. MongoDB is by far the most popular non-relational database. In short, a non-relational database allows you to start storing data without classifying the data into predefined tables. A non-relational database allows you to start without much initial time investment and provide many advantages if you’re dealing with large sets of heterogeneous data. Data retrieval is also simple and efficient no matter what the size of your total database.E - Express.js: Express is a node JS framework that allows web application development. If you search for something on YouTube, you’ll see that your URL is something like “https://www.youtube.com/results” Now, once your browser hits this URL, Express takes over, identifies that this is a search request, connects to the aforementioned database and gives an answer to your browser as to what to load. Express.js, in addition to having a very clean and readable syntax also provides an inbuilt folder and file structure for the Model View Controller(MVC) Framework. The MVC framework allows keeping the lines of code responsible for receiving the request separately, validating the data received in the request, and the functions for executing the business logic.A - AngularJS: AngularJS is a full-fledged front end development framework released in 2010. It is maintained by Google. AngularJS being a framework provides a lot of in-built functionalities which otherwise would take a lot of time and effort to build. AngularJS is one of the most popular front end frameworks. The popularity of AngularJS is due to its easy learning curve, compatibility across a wide range of browsers, and most importantly, a vibrant, actively contributing developer community.N - Node.js: Node.js is a cross-platform javascript runtime environment. To simplify, it allows javascript to be run outside the web browser and to be used for server-side scripting. Node.js has an event-driven architecture which allows it to rely on callbacks and not stall the application when any I/O operation is happening. This is called asynchronous I/O. This default architecture gives a massive advantage when user volume increases and your application has to scale. Other languages used for server-side scripting need external libraries to allow asynchronous execution. The E in our stack, Express.js is a framework on top of node.js.Why Is MEAN Stack popular?There are multiple reasons for this. Besides giving the software developer scope for improvement, it also holds the following benefits:Open source: All the frameworks are open-source which means they are free and have a multitude of developers contributing. MongoDB was also formerly open-source, but the language drivers are still available under an Apache License.Standardization: With MEAN stack ever increasing as a bundled skillset among developers, companies prefer to build their web application using the same stack to allow better collaboration between their developers. MEAN stack also makes it easier to change the development for a project from outsourced to in house and vice-versa.Seamless Integration: All components in the MEAN stack integrate smoothly. The server to the client (Express-AngularJS) and server to DB (Express-MongoDB) within MEAN stack is a combination which has been tried, tested, and scaled by countless companies. With MEAN stack gaining in popularity, more and more libraries are being published which make the integration even more seamless.Backed by Google: Node.js and AngularJS are backed by Google which helps a lot in keeping the developer community engaged and up to date with the latest versions. It also gives developers the confidence that the frameworks into which they’re pouring hours to master will not become obsolete.Should I Become a MEAN Stack Developer?The demand to create, improve, and maintain web applications is definitely not going to reduce in the coming years. Undertaking a MEAN Stack Web Development course qualifies you to be a certified MEAN stack web developer, which allows a prospective employer to hire a candidate with confidence that they will check all the boxes required to be an end to end web developer.
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MEAN Stack Web Development: A Beginner’s Guide

MEAN Stack Web Development: A Beginner’s Guide

Blog
A stack is all the programming languages, frameworks, libraries, tools, etc. used in software development of a particular project. MEAN stack is a combination environment with frameworks and a databas...
Continue reading