Transform your data into insights with essential techniques and math to unravel the secrets hidden within your data

Key Features

• ​Learn practical data science combined with data theory to gain maximum insight from data
• ​See how to deploy actionable machine learning pipelines while mitigating biases in data and models
• ​Explore actionable case studies and see how to put your new skills to use, fast!

Book Description

"Principles of Data Science" bridges mathematics, programming, and business analysis, empowering readers to confidently pose and address complex data questions and construct effective machine learning pipelines. It equips you with tools to transform abstract concepts and raw statistics into actionable insights.

Beginning with cleaning and preparing data + effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Discover the statistical models that help you take control and navigate even the densest (or the sparsest) datasets and find out how to create powerful visualizations that communicate the stories your data are telling. In this edition, you will also learn advanced transfer learning and pre-trained models for NLP and vision tasks, with a focus on application. Advanced techniques for mitigating algorithmic bias in data and models are covered, along with addressing model and data drift. Finally, you will explore medium-level data governance including data provenance, privacy, and deletion request handling.

By the end of the book, you'll learn the fundamentals of computational mathematics and statistics while exploring modern machine learning and large pre-trained models like GPT and BERT.

What you will learn

• ​Master data science's core steps with practical examples
• Bridge math and programming through advanced stats and ML
• Harness probability, calculus, and models for data control
• Explore transformative modern ML with large language models
• Evaluate ML success with effective metrics and MLOps
• Create visuals that convey actionable insights
• Quantify and mitigate biases in data and ML models

Who this book is for

​If you are an aspiring novice data scientist ready to learn more, this book is for you. If you have the basic math skills but want to apply them in data science, or you have good programming skills but lack the necessary math, this book will also help you. Some knowledge of Python programming will also help.

Table of Contents

1. Data Science Terminology
2. Types of Data
3. The Five Steps of Data Science
4. Basic Mathematics
5. Impossible or Improbable? - An Introduction to Probability
6. Advanced Probability
7. Basic Statistics
8. Advanced Statistics
9. Communicating Data
10. How to Tell If Your Toaster Is Learning: Machine Learning Essentials
11. Predictions Don’t Grow on Trees, or do they? - Beyond Statistical Modelling
12. Introduction to Transfer Learning and Pre-trained models
13. Tackling Model and Data Drift
14. Dealing with Data Governance
15. Dealing with Data Governance

About the Author

Sinan Ozdemir is an active lecturer focusing on large language models and a former lecturer of data science at the Johns Hopkins University. He is the author of multiple textbooks on data science and machine learning including "Quick Start Guide to LLMs". Sinan is currently the founder of LoopGenius which uses AI to help people and businesses boost their sales and was previously the founder of the acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a Master's Degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco.