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The specialists we contacted have various fields of expertise and are working in such companies as Facebook and Samsung. Our goal, however, was to get the feedback of industry experts.Īnd that’s why we interviewed data science practitioners - gurus, really -regarding the useful tools they choose for their projects. There is already a cornucopia of articles listing reliable machine learning tools with in-depth descriptions of their functionality. Data scientists have their own weapons - machine learning (ML) software.
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The best trained soldiers can’t fulfill their mission empty-handed. MemSQL: a database designed for real-time applications.Apache Spark: the tool for distributed computing.Caffe2: deep learning library with mobile deployment support.Keras: lightweight, easy-to-use library for fast prototyping.TensorBoard: a good tool for model training visualization.TensorFlow: flexible framework for large-scale machine learning.ML frameworks for neural network modeling.NLTK: Python-based human language data processing platform.scikit-learn: easy-to-use machine learning framework for numerous industries.NumPy: an extension package for scientific computing with Python.Frameworks for general machine learning.Tableau: powerful data exploration capabilities and interactive visualization.Jupyter notebook: collaborative work capabilities.
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matplotlib: a Python machine learning library for quality visualizations.pandas: a Python data analysis library enhancing analytics and modeling.R: a language for statistical computing and graphics.C++: a middle-level language used for parallel computing on CUDA.Python: a popular language with high-quality machine learning and data analysis libraries.The most popular machine learning languages.Seaborn aims to make visualization a central part of exploring and understanding data. It is a library for making attractive and informative statistical graphics in Python. Seaborn for statistical data visualization. Seaborn – For Statistical Data Visualization An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator. It is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. StatsModels – Statistical Modeling, Testing, and Analysis Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensional reduction. Scikit-learn – Machine Learning and Data Mining If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community. It is extensively used for data munging and preparation. Pandas for structured data operations and manipulations. Scipy is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices. This library also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++. The most powerful feature of NumPy is n-dimensional array. Numpy and Scipy – Fundamental Scientific Computing You should take some time to familiarize yourself with the basic purposes of these packages. Below are the major Python libraries that are used for working with data. There are libraries that give users the necessary functionality when crunching data. What makes Python extremely useful for working with data? Python is a general purpose language and is often used for things other than data analysis and data science.