Python Data Science Cookbook
Over 60 Practical Recipes to Help You Explore Python and Its Robust Data Science CapabilitieseBook - 2015
This book is intended for all levels of data science professionals, both students and practitioners from novice to experts. Different recipes in the chapters cater to the needs of different audiences. Novice readers can spend some time in getting themselves acquainted with data science in the first five chapters. Experts can refer to the later chapters to refer/understand how advanced techniques are implemented using Python. The book covers just enough mathematics and provides the necessary references for computer programmers who wish to understand data science. People from a non-Python background can effectively use this book. The first chapter of the book introduces Python as a programming language for data science. It will be helpful if you have some prior basic programming experience. The book is mostly self-contained and introduces data science to a new reader and can help him become an expert in this trade.What You Will LearnExplore the complete range of data science algorithmsManage and use Python libraries such as numpy, scipy, scikit learn, and matplotlib effectivelyTake a look at advanced regression methods for model building and variable selectionDevelop a thorough understanding of the underlying concepts and implementation of ensemble methodsSolve real-world problems using a variety of different datasets from numerical and text data modalitiesBecome accustomed to modern state-of-the art algorithms such as gradient boosting, random forest, rotation forest, and moreIn Detail
Python is increasingly becoming the language for data science.
This book will walk you through the various concepts, starting from simple algorithms, to the most complex available in the data science arsenal, to effectively mine data and derive intelligence from it.
The book begins by introducing you to the use of Python for data science, followed by how to work with Python environments. You will then learn how to analyze your data with Python. The book then teaches you about the concept of data mining, followed by extensive coverage of machine learning methods. It also covers the principles of shrinkage, ensemble methods, random forest, rotation forest, and extreme trees, which are must-haves for any successful data science professional.