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.

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