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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 8681 Top 25 Python Libraries for Machine Learning Blog
Raghav Bali 10 Jun 2019
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 difference

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 difference

Docker Vs Virtual Machine: Understand the difference

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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.
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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...
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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

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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...
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Become A Web Developer With NodeJS: The Blueprint To A Successful Career

If you’re a coder or developer, chances are that you already know about NodeJS or Node.js. It’s a JavaScript runtime that’s built on Chrome’s V8 JavaScript engine and uses an event-specific, non-blocking I/O model, which is why it’s lightweight and efficient. On the off chance you’re new to NodeJS though, let’s break that down for you. Node’s programming model is one of the primary reasons for its popularity. Because of its model, coders are able to subtract all the complex, error-prone concurrent programming issues that are associated with IO. Basically, you can get more IO scalability via its intuitive programming paradigm.What’s the Big Deal About NodeJS?What It’s NotA lot of confusion around NodeJS for newbies stems from understanding exactly what it is. It’s not a web-server and doesn’t accomplish anything by itself. Unlike Apache, you can’t use config files to point it to your HTML files. NodeJS is just another way to execute code on your desktop, i.e. a JavaScript runtime.Why It Became PopularIf you talk about NodeJS now, it’s nothing exciting or novel. In fact, it’s been around for eight years. But then, in a time dominated by Java, it was a game changer. At that time, web containers were everyone’s bane, your dependencies were still self-hosted and your build could have been Maven or Ant. Then, with the introduction of NodeJS, salvation was at hand. It enabled you to simply run your server and it would start almost instantly. It spelled out the end of interfaces, generics, and other ‘complex’ JVM dependencies.Now, it also enjoys a vibrant community with open source libraries for pretty much anything and it runs on many platforms like Windows, Linux, Unix, and Mac OS X.What NodeJS Is Mainly Used ForBecause it’s a brilliant server-side platform for developing real-time applications, developers can use NodeJS servers to scale massively. You can effectively handle thousands of real-time requests without hardware and extra hosting services that cost astronomical amounts. Node-based applications are also fully compatible with cloud services and can be added or deleted automatically, thereby preventing application spikes in the event of a traffic surge. It’s used for projects like:ChatsGamesVideoBig data streams without logicIt’s so powerful, fast, and scalable that even Netflix uses it to handle 15% of its global internet traffic.Why You Should take up NodeJs trainingIf you’re not convinced already, here are a few more reasons why you should consider getting NodeJS training:Market DemandBesides Netflix mentioned above, other global powerhouses like Uber, PayPal, and LinkedIn, all widely use NodeJS. If these huge brands are using it, then it’s obviously a popular technology already and something to keep in mind when you’re expanding your employability skill set and making career choices.It’s Easy to LearnNodeJS is written in JavaScript, which as everyone knows, is one of the most popular and wide reaching programming languages. So, even if you're a junior JavaScript developer, it will take you less time and effort to pick it up.Full StackEver wondered about the inception of full stack web development? You can give the credit to Node. To reiterate, full stack web development means a programmer who works on all aspects of the program, front-end, back-end, and database administration. Imagine the days before Node - full stack developers had to be adept in multiple languages.Vibrant CommunityAs mentioned above, NodeJS is an open source framework with an active global community, full of enthusiastic programmers who continuously contribute to its improvement. Not only will this make you feel more involved, but it makes learning easier and more fun for everyone!How to Start Learning NodeJSHave you made up your mind to take up the next step in advancing your career? Kudos! First, you can learn Javascript (since Node is written in Java). Once you understand the functions, module patterns, classes, promises, and callbacks, as well as capabilities of Strings, Numbers, Sets, and Maps, you can get trained formally in a NodeJS course. While you are getting trained, it’s important to keep in mind a few things:Understand non-blocking, which is the main feature in Node. This means understanding how I/O operations are performed asynchronously with lines of code adhering to a non-blocking patternLearn the concept of an event loop. To delve further, there is a stack, a heap, and a queue. In a loop, the queue, when polled for the next message, encounters it and sends the callback for that message to be executedLearn global variables and how to use the libraries that come with NodeJS.
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Become A Web Developer With NodeJS: The Blueprint To A Successful Career

Become A Web Developer With NodeJS: The Blueprint To A Successful Career

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If you’re a coder or developer, chances are that you already know about NodeJS or Node.js. It’s a JavaScript runtime that’s built on Chrome’s V8 JavaScript engine and uses an e...
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Top 5 Benefits Of Using AngularJS

With the number of internet and smartphone users on the rise, every corporation, major or otherwise, is increasingly looking to shift their mode of operations to web applications. Every software company is involved in some way or the other in developing web applications and this has lead to an increase in the demand for trained professionals who excel at app development.To enter the field of web development, you need to be familiar with HTML and at least one JavaScript framework. There are many JavaScript frameworks that are used by developers such as React, Ember, Backbone, Knockout, Angular, etc. Out of these, AngularJs has emerged to be a fan favorite. Many developers swear by this JavaScript framework and its many advantages. Angular training has the potential to vastly improve your employability. It is an advanced script that works with HTML to improve its performance while also simplifying the whole UI development process from its design to testing. Here are some indisputable benefits that AngularJs has over the other frameworks.Benefits of AngularJs1. High PerformanceAngularJs is one of the more advanced JavaScript frameworks that offers advanced features such as filters, data binding, routing, directives, animations, form validation, etc. These features reduce the time it takes to create a web application while also greatly simplifying the process. This framework is also very robust and requires very little time to be spent on debugging. At the same time, adding new features to the existing application and making minor modifications is very effortless.2. Supports Single Page ApplicationsSingle Page Application or SPAs is a type of web application that has become very popular in recent times and for good reason. The SPAs load a single HTML page that is then updated depending on the input from the user. This cuts down the page loading time as the page will be more responsive. They also reduce the network traffic and server load since the applications are rendered on the client end.AngularJs supports SPAs and it is also in a unique position to offer more. Sometimes, web applications are large and cannot be built as a SPA. However, even these can be built as hybrid SPA applications. The application is divided into smaller sections and each of these sections is built as a SPA. Features of AngularJs such as routing, templates, journaling, etc. become very useful here to integrate these SPAs into a large app.3. Handles DependenciesAngularJs has an in-built dependency injection system that impacts how the application is wired. The framework provides the dependencies to the developers upon request. This reduces the load on the server which, in turn, makes the application faster. The dependency injection becomes even more helpful during testing and while building SPAs. You can split the application into smaller modules and use dependency injection to test the modules independently.4. Architecture That Reduces Line CodingAngularJs uses an architecture that is a combination of MVC(Model-View-Controller) and MVVM(Model-View-ViewModel). You will only have to divide your code into MVC components and the AngularJs takes care of providing the MVC pipeline. This reduces the amount of line coding to be performed by the developer. Want to create robust web applications without writing a ton of code? Go for AngularJs training.5. Supports Parallel DevelopmentOne of the biggest advantages of AngularJs is its ability to break down an action into its services and sub-controllers. The developers working on the application can code and test their parts independent of the other’s work. This makes it easier to scale the project and streamlines the workflow.How Can AngularJs Training Impact Your Career?As per ITJobsWatch, AngularJs is responsible for almost 7% of all IT jobs that were advertised till May 2019 and the average salary was around 52,000 Euros per annum. The average salary has also witnessed a growth of 5%. Compared to other JavaScript frameworks, AngularJs is a highly demanded skill set in the job market. The most required skill set in the IT industry right now is related to web application development and AngularJs leads the pack here. Getting trained in AngularJs course can only do wonders for your career.AngularJs is built and maintained by Google, and there is an immense amount of trust that is placed on it. Add this to all the various advantages of AngularJs, it is not surprising at all that this has emerged as one of the most sought after qualifications for web development jobs. While learning Angular, it is important that you learn all the versions that are currently in use, as well as the latest version. This will help you understand the code written on the older versions and will equip you to create new apps using the latest version.If you have some basic HTML and CSS knowledge and if your JavaScript knowledge is at an intermediate level, then you should definitely consider learning AngularJs to further your career.
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Top 5 Benefits Of Using AngularJS

Top 5 Benefits Of Using AngularJS

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With the number of internet and smartphone users on the rise, every corporation, major or otherwise, is increasingly looking to shift their mode of operations to web applications. Every software compa...
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Top 9 Benefits Of Learning Apache Spark and Scala

What Is Apache Spark and Scala All About?Big Data and Analytics are transforming the way businesses take informed market-oriented decisions, craft strategies for targeting customer segments that are optimally promising, and remain shielded from market quirks and economic volatilities. These abilities are impacted by mining information that is locked in large data volumes generated online or from other connected sources.Big Data can be reliably processed with the Apache Spark interface. Apart from facilitating seamless programming for data clusters, Spark also offers proper tolerance for faults and data parallelism. This implies that large datasets can be processed speedily by this open source platform. Apache Spark has an edge over Hadoop in terms of better and sophisticated capabilities on data handling, storing, evaluation and retrieving fronts. Spark framework comes integrated with modules for ML (Machine Learning), real-time data streaming, textual and batch data, graphics, etc., which makes it ideal for different industry verticals.Scala or Scalable Language is a general-purpose object-oriented language with which Spark is written for supporting cluster computing. Scala offers support with immutability, type interference, lazy evaluation, pattern matching, and other features. Features absent in Java such as operator overloading, named parameters, no checked exceptions, etc. are also offered by Scala.Why Should I Learn Apache Spark and Scala?Data science offers unparalleled scope if you want to scale new heights in your career. Also, as part of an organization, if you are strategizing on cornering your niche market, you need to get focused insights into how the market is changing. With Apache Spark and Scala training, you can become proficient in analyzing patterns and making conclusive fact-driven assumptions.There are many incentives for learning this framework-language combination as an aspirant or by exposing your organization’s chosen employees to this.1) Ideal for Implementing IoTIf your company is focusing on the Internet of Things, Spark can drive it through its capability of handling many analytics tasks concurrently. This is accomplished through well-developed libraries for ML, advanced algorithms for analyzing graphs, and in-memory processing of data at low latency.2) Helps in Optimizing Business Decision MakingLow latency data transmitted by IoT sensors can be analysed as continuous streams by Spark. Dashboards that capture and display data in real time can be created for exploring improvement avenues.3) Complex Workflows Can Be Created with EaseSpark has dedicated high-level libraries for analyzing graphs, creating queries in SQL, ML, and data streaming. As such, you can create complex big data analytical workflows with ease through minimal coding.4) Prototyping Solutions Becomes EasierAs a Data Scientist, you can utilize Scala’s ease of programming and Spark’s framework for creating prototype solutions that offer enlightening insights into the analytical model.5) Helps in De-Centralized Processing of DataIn the coming decade, Fog computing would gain steam and will complement IoT to facilitate de-centralized processing of data. By learning Spark, you can remain prepared for upcoming technologies where large volumes of distributed data will need to be analyzed. You can also devise elegant IoT driven applications to streamline business functions.6) Compatibility with HadoopSpark can function atop HDFS (Hadoop Distributed File System) and can complement Hadoop. Your organization need not spend additionally on setting up Spark infrastructure if Hadoop cluster is present. In a cost-effective manner, Spark can be deployed on Hadoop’s data and cluster.7) Versatile FrameworkSpark is compatible with multiple programming languages such as R, Java, Python, etc. This implies that Spark can be used for building Agile applications easily with minimal coding. The Spark and Scala online community is very vibrant with numerous programmers contributing to it. You can get all the required resources from the community for driving your plans.8) Faster Than HadoopIf your organization is looking to enhance data processing speeds for making faster decisions, Spark can definitely offer a leading edge. Data is processed in Spark in a cyclic manner and the execution engine shares data in-memory. Support for Directed Acyclic Graph (DAG) mechanism allows Spark engine to process simultaneous jobs with the same datasets. Data is processed by Spark engine 100x quicker compared to Hadoop MapReduce.9) Proficiency EnhancerIf you learn Spark and Scala, you can become proficient in leveraging the power of different data structures as Spark is capable of accessing Tachyon, Hive, HBase, Hadoop, Cassandra, and others. Spark can be deployed over YARN or another distributed framework as well as on a standalone server.Learn Apache Spark and Scala To Widen Your Performance HorizonCompleting an Apache Spark and Scala course from a renowned learning center would make you competent in leveraging Spark through practice sessions and real-life exercises. Once you become capable of using this cutting-edge analytics framework, securing lucrative career opportunities won’t be a challenge. Also, if you belong to an organization, gaining actual and actionable insights for decision making would be a breeze.
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Top 9 Benefits Of Learning Apache Spark and Scala

Top 9 Benefits Of Learning Apache Spark and Scala

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What Is Apache Spark and Scala All About?Big Data and Analytics are transforming the way businesses take informed market-oriented decisions, craft strategies for targeting customer segments that are o...
Continue reading