Python for Data Science For Dummies

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.

Part 1: Getting Started with Data Science and Python

CHAPTER 1: Discovering the Match between Data Science and Python

CHAPTER 2: Introducing Python’s Capabilities and Wonders

CHAPTER 3: Setting Up Python for Data Science

CHAPTER 4: Working with Google Colab

Part 2: Getting Your Hands Dirty with Data

CHAPTER 5: Working with Jupyter Notebook

CHAPTER 6: Working with Real Data

CHAPTER 7: Processing Your Data

CHAPTER 8: Reshaping Data

CHAPTER 9: Putting What You Know into Action

Part 3: Visualizing Information

CHAPTER 10: Getting a Crash Course in Matplotlib

CHAPTER 11: Visualizing the Data

Part 4: Wrangling Data

CHAPTER 12: Stretching Python’s Capabilities

CHAPTER 13: Exploring Data Analysis

CHAPTER 14: Reducing Dimensionality

CHAPTER 15: Clustering

CHAPTER 16: Detecting Outliers in Data

Part 5: Learning from Data

CHAPTER 17: Exploring Four Simple and Effective Algorithms

CHAPTER 18: Performing Cross-Validation, Selection, and Optimization

CHAPTER 19: Increasing Complexity with Linear and Nonlinear Tricks

CHAPTER 20: Understanding the Power of the Many

Part 6: The Part of Tens

CHAPTER 21: Ten Essential Data Resources

CHAPTER 22: Ten Data Challenges You Should Take

About the Authors

John Paul Mueller is a freelance author and technical editor who has written 124 books on topics ranging like networking, home security, database management, and heads-down programming.

 Luca Massaron is a data scientist specialized in solving real-world problems with AI, machine learning, and algorithms. He is also a Kaggle Grandmaster and a Google Developer Expert.