Career Path in Data Science for Data Scientists
By Susan MayData 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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Data Science Vs Machine Learning Vs Artificial Intelligence
By Animikh AichWhat 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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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 ...Continue reading
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Docker Vs Virtual Machine: Understand the differences
By Susan MayVirtual 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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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,...Continue reading
Python in a Nutshell: Everything That You Need to Know
By Susan MayPython 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 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...Continue reading
Top 5 Benefits of Data Science With Python Foundation Training
By Susan MayThere 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.
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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...Continue reading
The Ultimate Guide to Node.Js
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Top 6 Benefits of Learning Data Science with Python
By Susan MayOver 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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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...Continue reading
Top 8 Advantages Of Learning React JS
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MEAN Stack Web Development: A Beginner’s Guide
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