Starting Data Analytics with Generative AI and Python

Artur Guja, Marlena Siwiak, Marian Siwiak

• Publisher: Manning
• Year: 2024
• Topic: Computer Science
• eBook ISBN:
  9781638356424
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Accelerate your mastery of data analytics with the power of ChatGPT.

Whether you’re brand new to data analysis or an experienced pro looking to do more work, faster, Starting Data Analytics with Generative AI and Python is here to help simplify and speed up your data analysis! Written by a pair of world-class data scientists and an experienced risk manager, the book concentrates on the practical analytics tasks you'll do every day.

Inside Starting Data Analytics with Generative AI and Python you’ll learn how to:

• Write great prompts for ChatGPT
• Perform end-to-end descriptive analytics
• Set up an AI-friendly data analytics environment
• Evaluate the quality of your data
• Develop a strategic analysis plan
• Generate code to analyze non-text data
• Explore text data directly with ChatGPT
• Prepare reliable reports

In Starting Data Analytics with Generative AI and Python you’ll learn how to improve your coding efficiency, generate new analytical approaches, and fine-tune data pipelines—all assisted by AI tools like ChatGPT. For each step in the data process, you’ll discover how ChatGPT can implement data techniques from simple plain-English prompts. Plus, you’ll develop a vital intuition about the risks and errors that still come with these tools.

About the technology

If you have basic knowledge of data analysis, this book will show you how to use ChatGPT to accelerate your essential data analytics work. This speed-up can be amazing: the authors report needing one third or even one quarter the time they needed before.

About the book

You’ll find reliable and practical advice that works on the job. Improve problem exploration, generate new analytical approaches, and fine-tune your data pipelines—all while developing an intuition about the risks and errors that still come with AI tools. In the end, you’ll be able to do significantly more work, do it faster, and get better results, without breaking a sweat.

Assuming only that you know the foundations, this friendly book guides you through the entire analysis process—from gathering and preparing raw data, data cleaning, generating code-based solutions, selecting statistical tools, and finally creating effective data presentations. With clearly-explained prompts to extract, interpret, and present data, it will raise your skills to a whole different level.

What's inside

• Write great prompts for ChatGPT
• Perform end-to-end descriptive analytics
• Set up an AI-friendly data analytics environment
• Evaluate the quality of your data
• Develop a strategic analysis plan
• Generate code to analyze non-text data
• Explore text data directly with ChatGPT
• Prepare reliable reports

About the author

Authors Artur Guja, Dr. Marlena Siwiak, and Dr. Marian Siwiak are experienced data scientists with backgrounds in business, scientific research, and finance.

The technical editor on this book was Mike Jensen.

Table of Contents

1 Introduction to the use of generative AI in data analytics
2 Using generative AI to ensure sufficient data quality
3 Descriptive analysis and statistical inference supported by generative AI
4 Using generative AI for result interpretations
5 Basic text mining using generative AI
6 Advanced text mining with generative AI
7 Scaling and performance optimization
8 Risk, mitigation, and tradeoffs
Appendix A Specifying multiple DataFrames to ChatGPT v4
Appendix B On debugging ChatGPT’s code
Appendix C On laziness and human errors

Table of contents

• copyright
• dedication
• contents
• foreword
• preface
• acknowledgments
• about this book
• about the authors
• about the cover illustration
• 1 Introduction to the use of generative AI in data analytics
• 2 Using generative AI to ensure sufficient data quality
• 3 Descriptive analysis and statistical inference supported by generative AI
• 4 Using generative AI for result interpretations
• 5 Basic text mining using generative AI
• 6 Advanced text mining with generative AI
• 7 Scaling and performance optimization
• 8 Risk, mitigation, and tradeoffs
• appendix A Specifying multiple DataFrames to ChatGPT v4
• appendix B On debugging ChatGPT’s code
• appendix C On laziness and human errors
• index