This is the code repository for Principles of Data Science, published by Packt.
A beginner’s guide to essential math and coding skills for data fluency and machine learning
Principles of Data Science provides an end-to-end framework for cultivating critical thinking about data, performing practical data science, building performant machine learning models, and mitigating bias in AI pipelines. Learn the fundamentals of computational math and stats while exploring modern machine learning and large pre-trained models
This book covers the following exciting features:
- Master the fundamentals steps of data science through practical examples
- Bridge the gap between math and programming using advanced statistics and ML
- Harness probability, calculus, and models for effective data control
- Explore transformative modern ML with large language models
- Evaluate ML success with impactful metrics and MLOps
- Create compelling visuals that convey actionable insights
- Quantify and mitigate biases in data and ML models
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter01.
The code will look like the following:
['RT', '@robdv:', '$TWTR', 'now', 'top', 'holding', 'for', 'Andor,',
'unseating', '$AAPL']
Following is what you need for this book: If you are an aspiring novice data scientist eager to expand your knowledge, this book is for you. Whether you have basic math skills and want to apply them in the field of data science, or you excel in programming but lack the necessary mathematical foundations, you’ll find this book useful. Familiarity with Python programming will further enhance your learning experience.
With the following software and hardware list you can run all code files present in the book (Chapter 1-15).
| Chapter | Software required | OS required |
|---|---|---|
| 1-15 | Python v3.4 or above | Windows, macOS, or Linux |
- Page 31 (Paragraph 6, line 1): geometric mean, which is the square root of the product of all the values. should be geometric mean, which is the nth root of the product of all the values.
Sinan Ozdemir is an active lecturer on large language models and a former lecturer of data science at 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.