Python for Data Science For Dummies, 3rd Edition

  • 6h 46m
  • John Paul Mueller, Luca Massaron
  • John Wiley & Sons (US)
  • 2023

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.

About the Author

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.

In this Book

  • Introduction
  • Discovering the Match between Data Science and Python
  • Introducing Python’s Capabilities and Wonders
  • Setting Up Python for Data Science
  • Working with Google Colab
  • Working with Jupyter Notebook
  • Working with Real Data
  • Processing Your Data
  • Reshaping Data
  • Putting What You Know into Action
  • Getting a Crash Course in Matplotlib
  • Visualizing the Data
  • Stretching Python’s Capabilities
  • Exploring Data Analysis
  • Reducing Dimensionality
  • Clustering
  • Detecting Outliers in Data
  • Exploring Four Simple and Effective Algorithms
  • Performing Cross-Validation, Selection, and Optimization
  • Increasing Complexity with Linear and Nonlinear Tricks
  • Understanding the Power of the Many
  • Ten Essential Data Resources
  • Ten Data Challenges You Should Take
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