Combine the best of human data analysis with the power of AI and ChatGPT.

Starting Data Analytics with Generative AI and Python teaches you how to accelerate essential data analytics tasks using ChatGPT. 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
• Developing a strategic analysis plan
• Generate code to analyze non-text data
• Explore text data directly with ChatGPT
• Prepare reliable reports

Starting Data Analytics with Generative AI and Python shows you how to improve the efficiency and outcomes of your data analytics by using ChatGPT. There’s no AI hype here. Instead, you’ll find reliable and practical advice that works on the job. Learn how to improve your coding efficiency, generate new analytical approaches, and fine-tune your pipelines—all while developing an intuition about the risks and errors that still come with these tools. In the end, you’ll be able to do more work, do it faster, and get better results.

About the technology

Generative Pre-trained Transformer (GPT) AI models like ChatGPT can turbocharge your analytical workflow by generating insights in minutes that might otherwise take hours or days of work and research. A data analyst can use ChatGPT for a huge variety of tasks, including planning and designing analyses, generating code-based solutions, and selecting statistical tools.

About the book

Starting Data Analytics with Generative AI and Python is a guide to enhancing and accelerating data analytics using ChatGPT. This book walks you through the full analytics pipeline, demonstrating the discipline’s core tools and methods with an easy-to-understand e-commerce example. For each step in the process, you’ll discover how ChatGPT can implement data techniques from simple plain-English prompts.

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. Each topic is illustrated with transcripts of authentic ChatGPT conversations, showing best practice prompt engineering in action. You'll rapidly realize ChatGPT's potential and learn how and when to apply it appropriately. The book provides vital guidance on the model’s risks and mistakes so you can make the most of its capabilities without being blindsided by its shortcomings.

Table of Contents

1. Introduction to the use of generative Al in data analytics

2. Using generative Al to ensure sufficient data quality

3. Descriptive analysis and statistical inference supported by generative Al

4. Using generative Al for result interpretations

5. Basic text mining using generative Al

6. Advanced text mining with generative Al

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

About the reader

Whether you're a data expert seeking efficiency gains, an analyst looking to extend your spreadsheets with serious data tools, or a complete beginner making a ChatGPT-empowered career jump, this book is for you.

About the author

Artur Guja is a risk manager, computer scientist, systems developer, and financial markets professional with over 20 years of experience in the banking sector, delivering safe and practical solutions across IT, risk management, and financial product trading.

Dr. Marlena Siwiak is an experienced data scientist and bioinformatician with a comprehensive scientific background who gained experience in developing business data applications. An individual who knows how to use both numbers and words.

Dr. Marian Siwiak is a data scientist and strategist with a track record of using data knowledge and managerial experience to successfully deliver multimillion IT, scientific and technical projects covering various areas, from life sciences to robotics.