Data Science Books

Data Science Projects with Python: A case study approach to successful data science projects using Python, pandas, and scikit-learn

Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key Features Learn techniques to use data to identify the exact problem to be solved Visualize data using different graphs Identify how to select an appropriate algorithm for data extraction Book Description Data Science Projects with Python is designed to give you practical …

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Practical Data Science with SAP: Machine Learning Techniques for Enterprise Data

Learn how to fuse today’s data science tools and techniques with your SAP enterprise resource planning (ERP) system. With this practical guide, SAP veterans Greg Foss and Paul Modderman demonstrate how to use several data analysis tools to solve interesting problems with your SAP data.Data engineers and scientists will explore ways to add SAP data to their analysis …

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Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, …

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Data Science at Scale with Python and Dask

Large datasets tend to be distributed, non-uniform, and prone to change. Dask simplifies the process of ingesting, filtering, and transforming data, reducing or eliminating the need for a heavyweight framework like Spark. Data Science at Scale with Python and Dask teaches readers how to build distributed data projects that can handle huge amounts of data. The book …

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Machine Learning Algorithms – Second Edition: A reference guide to popular algorithms for data science and machine learning

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features Explore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression Study patterns and make predictions across various datasets Book Description Machine learning has gained tremendous popularity for its …

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The Future of Data Science and Parallel Computing

Dr. Ganapathi Pulipaka is a Chief Data Scientist and SAP Technical Lead for one of the largest firms in the world. He is also a PostDoc Research Scholar in Computer Science Engineering in Big Data Analytics, Machine Learning, Robotics, IoT, Artificial Intelligence as part of Doctor of Computer Science program from Colorado Technical University, CO with another PhD …

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