Principles of Data Science. A beginner's guide to essential math and coding skills for data fluency and machine learning - Third Edition Katowice

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. Spis treści: 1. Data Science Terminology2. Types of Data3. The Five Steps of Data Science4. Basic Mathematics5. Impossible or Improbable – A Gentle Introduction to Probability6. Advanced Probability7. What are the Chances? An Introduction to Statistics8. Advanced Statistics9. Communicating Data10. How to Tell if Your Toaster is Learning – Machine Learning Essentials11. Predictions Don't Grow on Trees, or Do They?12. Introduction to Transfer Learning and Pre-trained Models13. Mitigating Algorithmic Bias and Tackling Model and Data Drift14. AI Governance15. Navigating Real-World Data Science Case Studies in Action O autorze: Sinan Ozdemir is a data scientist, start-up founder, and educator living in the San Francisco Bay Area. He studied pure mathematics at the Johns Hopkins University. He then spent several years conducting lectures on data science there, before founding his own start-up, Kylie ai, which uses artificial intelligence to clone brand personalities and automate customer service communications. He is also the author of Principles of Data Science, available through Packt.