The fast and easy way to learn Python programming and statistics

Python is a general-purpose programming language created in the late 1980sand named after Monty Pythonthat's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library.

Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud.

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.

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.

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 Pythons 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 Pythons 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