Principles of Data Science: A beginner’s guide to essential math and coding skills for data fluency and machine learning, 3rd Edition

English | 2024 | ISBN: 978-1837636303 | 326 Pages | PDF, EPUB | 40 MB

Transform your data into insights with must-know techniques and mathematical concepts to unravel the secrets hidden within your data

Key Features:

• Learn practical data science combined with data theory to gain maximum insights from data
• Discover methods for deploying actionable machine learning pipelines while mitigating biases in data and models
• Explore actionable case studies to put your new skills to use immediately

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

Starting with cleaning and preparation, you’ll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you’ll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data.

With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You’ll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you’ll explore medium-level data governance, including data provenance, privacy, and deletion request handling.

By the end of this data science book, you’ll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.

What You Will Learn:

• 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