Python for Data Science for Dummies （3RD）

Mueller, John Paul/ Massaron, Luca

For Dummies（2023/11発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 464 p.
• 言語 ENG
• 商品コード 9781394213146
• DDC分類 005.133

Full Description

Let Python do the heavy lifting for you as you analyze large datasets

Python for Data Science For Dummies lets you get your hands dirty with data using one of the top programming languages. This beginner's guide takes you step by step through getting started, performing data analysis, understanding datasets and example code, working with Google Colab, sampling data, and beyond. Coding your data analysis tasks will make your life easier, make you more in-demand as an employee, and open the door to valuable knowledge and insights. This new edition is updated for the latest version of Python and includes current, relevant data examples.

Get a firm background in the basics of Python coding for data analysis
Learn about data science careers you can pursue with Python coding skills
Integrate data analysis with multimedia and graphics
Manage and organize data with cloud-based relational databases

Python careers are on the rise. Grab this user-friendly Dummies guide and gain the programming skills you need to become a data pro.

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 33

Chapter 4: Working with Google Colab 49

Part 2: Getting Your Hands Dirty with Data 71

Chapter 5: Working with Jupyter Notebook 73

Chapter 6: Working with Real Data 83

Chapter 7: Processing Your Data 105

Chapter 8: Reshaping Data 131

Chapter 9: Putting What You Know into Action 143

Part 3: Visualizing Information 157

Chapter 10: Getting a Crash Course in Matplotlib 159

Chapter 11: Visualizing the Data 177

Part 4: Wrangling Data 199

Chapter 12: Stretching Python's Capabilities 201

Chapter 13: Exploring Data Analysis 223

Chapter 14: Reducing Dimensionality 251

Chapter 15: Clustering 273

Chapter 16: Detecting Outliers in Data 291

Part 5: Learning from Data 305

Chapter 17: Exploring Four Simple and Effective Algorithms 307

Chapter 18: Performing Cross-Validation, Selection, and Optimization 327

Chapter 19: Increasing Complexity with Linear and Nonlinear Tricks 351

Chapter 20: Understanding the Power of the Many 391

Part 6: The Part of Tens 413

Chapter 21: Ten Essential Data Resources 415

Chapter 22: Ten Data Challenges You Should Take 421

Index 431