Data science is an interdisciplinary field encompassing scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured. It draws principles from mathematics, statistics, information science, computer science, machine learning, visualization, data mining, and predictive analytics. However, it is fundamentally grounded in mathematics. This book explains and applies the fundamentals of data science crucial for technical professionals such as DBAs and developers who are making career moves toward practicing data science. It is an example- driven book providing complete Python coding examples to complement and clarify data science concepts, and enrich the learning experience. Coding examples include visualizations whenever appropriate. The book is a necessary precursor to applying and implementing machine learning algorithms, because it introduces the reader to foundational principles of the science of data.

This book is an introduction to concepts, techniques, and applications in data science. This book focuses on the analysis of data, covering concepts from statistics to machine learning, techniques for graph analysis and parallel programming, and applications such as recommender systems or sentiment analysis. All chapters introduce new concepts that are illustrated by practical cases using real data. Public databases such as Eurostat, different social networks, and MovieLens are used. Specific questions about the data are posed in each chapter. The solutions to these questions are implemented using Python programming language and presented in code boxes properly commented. This allows the reader to learn data science by solving problems which can generalize to other problems. This book is not intended to cover the whole set of data science methods neither to provide a complete collection of references. Currently, data science is an increasing and emerging field, so readers are encouraged to look for specific methods and references using keywords in the net.

r for data science 中文翻译版~

版权归作者所有，任何形式转载请联系作者。 作者：Tommy（来自豆瓣） 来源：https://book.douban.com/review/8367790/ 本书内容对应的 Jupyter notebook 放在 GitHub 上。 https://github.com/jakevdp/PythonDataScienceHandbook

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. Why exploratory data analysis is a key preliminary step in data science; How random sampling can reduce bias and yield a higher quality dataset, even with big data; How the principles of experimental design yield definitive answers to questions; How to use regression to estimate outcomes and detect anomalies; Key classification techniques for predicting which categories a record belongs to; Statistical machine learning methods that "learn" from data; Unsupervised learning methods for extracting meaning from unlabeled data.

kaggle 冠军 owen zhang 关于kaggle比赛数据处理的ppt

统计学习经典教程，小说风格，非扫描高清版，自购收藏。 Will Zach find Alice, the missing love of his life, and save the world? Will he survive the bridge of death? Can he escape the zombie horde? Statistically speaking the odds don’t look good…. Reluctant hero Zach Slade wakes up to find that his soul mate Alice has vanished. To find her, he must solve a puzzle using the only clue he has – Alice’s unfinished research report. If only he hadn’t skipped science class to form a band. The more Zach unravels the enigma of reality, the more he sense that something is very wrong. Did Alice ever exist? Who is the mysterious Professor Milton? What is causing people to forget who they are? And why is everyone intent on teaching him statistics? Join Zach on his bizarre journey … It will transform your understanding of statistics forever.

Review, 'A must-read resource for anyone who is serious about embracing the opportunity of big data.', -- Craig Vaughan, Global Vice President at SAP, 'This book goes beyond data analytics 101. It's the essential guide for those of us (all of us?) whose businesses are built on the ubiquity of data o

R for Data Science.epub Advanced R.epub R packages.epub

What is data science? With the major technological advances of the last two decades, coupled in part with the internet explosion, a new breed of analysist has emerged. The exact role, background, and skill-set, of a data scientist are still in the process of being defined and it is likely that by the time you read this some of what we say will seem archaic.