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Data Science for Decision Makers

You're reading from Data Science for Decision Makers Enhance your leadership skills with data science and AI expertise

Product type Paperback

Published in Jul 2024

Publisher Packt

ISBN-13 9781837637294

Length 270 pages

Edition 1st Edition

Languages

Python

Concepts

Artificial Intelligence

Author (1):

Jon Howells

View More author details

Table of Contents (20) Chapters

Preface

1. Part 1: Understanding Data Science and Its Foundations

2. Chapter 1: Introducing Data Science FREE CHAPTER

3. Chapter 2: Characterizing and Collecting Data

4. Chapter 3: Exploratory Data Analysis

5. Chapter 4: The Significance of Significance

6. Chapter 5: Understanding Regression

7. Part 2: Machine Learning – Concepts, Applications, and Pitfalls

8. Chapter 6: Introducing Machine Learning

9. Chapter 7: Supervised Machine Learning

10. Chapter 8: Unsupervised Machine Learning

11. Chapter 9: Interpreting and Evaluating Machine Learning Models

12. Chapter 10: Common Pitfalls in Machine Learning

13. Part 3: Leading Successful Data Science Projects and Teams

14. Chapter 11: The Structure of a Data Science Project

15. Chapter 12: The Data Science Team

16. Chapter 13: Managing the Data Science Team

17. Chapter 14: Continuing Your Journey as a Data Science Leader

18. Index

Why subscribe?

19. Other Books You May Enjoy

Understanding Regression

In this chapter, we will learn about regression, a powerful statistical tool that can enable decision-makers to identify and understand relationships between variables, uncover trends, and forecast future trends.

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Authors (1)

Howells

Howells

Jon Howells is a seasoned AI and Data Science professional with a decade of experience in the field. He runs an AI consultancy called Qualifai and has worked with various companies, including Unilever, Permira and Capgemini, developing and deploying data science services and solutions. He holds a Master's degree in Computational Statistics & Machine Learning from UCL. Jon is particularly interested in the application of Large Language Models (LLMs) in consumer-focused businesses, such as using LLMs for consumer research and feedback analysis, personalized content generation, and enhanced customer support, ultimately helping businesses better understand and engage with their customers.

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