The Data Science Handbook

• Title: The Data Science Handbook
• Author: Field Cady
• Length: 416 pages
• Edition: 1
• Language: English
• Publisher: Wiley
• Publication Date: 2017-02-28
• ISBN-10: 1119092949
• ISBN-13: 9781119092940
  

A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline

Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline.

Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features:

• Extensive sample code and tutorials using Python™ along with its technical libraries
• Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems
• Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity
• A wide variety of case studies from industry
• Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed
  

The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set.

FIELD CADY is Principal Data Scientist at Maana, Inc. where he applies Big Data tools to solve industrial problems. He has a BS in Physics from Stanford University, an MS in Applied Mathematics from the University of Washington, and an MS in Computer Science from Carnegie Mellon University.

Table of Contents

Chapter 1 Introduction: Becoming a Unicorn

Part I The Stuff You’ll Always Use
Chapter 2 The Data Science Road Map
Chapter 3 Programming Languages
Chapter 4 Data Munging: String Manipulation, Regular Expressions, and Data Cleaning
Chapter 5 Visualizations and Simple Metrics
Chapter 6 Machine Learning Overview
Chapter 7 Interlude: Feature Extraction Ideas
Chapter 8 Machine Learning Classification
Chapter 9 Technical Communication and Documentation

Part II Stuff You Still Need to Know
Chapter 10 Unsupervised Learning: Clustering and Dimensionality Reduction
Chapter 11 Regression
Chapter 12 Data Encodings and File Formats
Chapter 13 Big Data
Chapter 14 Databases
Chapter 15 Software Engineering Best Practices
Chapter 16 Natural Language Processing
Chapter 17 Time Series Analysis
Chapter 18 Probability
Chapter 19 Statistics
Chapter 20 Programming Language Concepts
Chapter 21 Performance and Computer Memory

Part III Specialized or Advanced Topics
Chapter 22 Computer Memory and Data Structures
Chapter 23 Maximum Likelihood Estimation and Optimization
Chapter 24 Advanced Classifiers
Chapter 25 Stochastic Modeling
Chapter 25a Parting Words: Your Future as a Data Scientist