Python for Data Science For Dummies

by John Paul Mueller, Luca Massaron

Overview

Python is a general-purpose programming language that is popular with data scientists. It is free, as are a number of open-source libraries help acquire, organize, and process information. This book is designed for beginners to data analysis and covers the basics of Python data analysis programming and statistics. The book covers the Python fundamentals that are necessary to data analysis, including objects, functions, modules, and libraries.

The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.

The remainder of the book provides examples and tips and tricks for doing interesting things with data analysis programming. The topics are arranged into the following parts:

PART I: Getting Started with Data Science & Python

PART II: Getting Your Hands Dirty with Data

PART III: Visualizing Information

PART IV: Wrangling Data

PART V: Learning from Data

PART VI: Part of Tens

Product Details

ISBN-13:	9781119547624
Publisher:	Wiley
Publication date:	02/27/2019
Series:	For Dummies Books
Edition description:	2nd ed.
Pages:	496
Product dimensions:	7.30(w) x 9.20(h) x 1.00(d)

About the Author

John Paul Mueller is a tech editor and the author of over 100 books on topics from networking and home security to database management and heads-down programming. Follow John's blog at http: //blog.johnmuellerbooks.com/. Luca Massaron is a data scientist who specializes in organizing and interpreting big data and transforming it into smart data. He is a Google Developer Expert (GDE) in machine learning.

Table of Contents

Introduction 1

Part 1: Getting Started with Data Science and Python 7

Chapter 1: Discovering the Match between Data Science and Python 9

Chapter 2: Introducing Python’s Capabilities and Wonders 21

Chapter 3: Setting Up Python for Data Science 39

Chapter 4: Working with Google Colab 59

Part 2: Getting Your Hands Dirty with Data 81

Chapter 5: Understanding the Tools 83

Chapter 6: Working with Real Data 99

Chapter 7: Conditioning Your Data 121

Chapter 8: Shaping Data 149

Chapter 9: Putting What You Know in Action 169

Part 3: Visualizing Information 183

Chapter 10: Getting a Crash Course in MatPlotLib 185

Chapter 11: Visualizing the Data 201

Part 4: Wrangling Data 227

Chapter 12: Stretching Python’s Capabilities 229

Chapter 13: Exploring Data Analysis 251

Chapter 14: Reducing Dimensionality 275

Chapter 15: Clustering 295

Chapter 16: Detecting Outliers in Data 313

Part 5: Learning from Data 327

Chapter 17: Exploring Four Simple and Effective Algorithms 329

Chapter 18: Performing Cross-Validation, Selection, and Optimization 347

Chapter 19: Increasing Complexity with Linear and Nonlinear Tricks 371

Chapter 20: Understanding the Power of the Many 411

Part 6: The Part of Tens 429

Chapter 21: Ten Essential Data Resources 431

Chapter 22: Ten Data Challenges You Should Take 437

Index 447