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

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
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description:

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

Who this book is for:

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.

將您提供的文字翻譯成繁體中文如下：

將您的數據轉化為洞察力，掌握必備技巧和數學概念，揭示數據中隱藏的秘密。

主要特點：
- 學習實用的數據科學和數據理論，從數據中獲取最大的洞察力。
- 發現部署可行的機器學習流程的方法，同時減輕數據和模型中的偏見。
- 通過實際案例研究，立即應用您的新技能。
- 購買印刷版或Kindle電子書，可獲得免費的PDF電子書。

書籍描述：
《數據科學原理》將數學、編程和業務分析相結合，使您能夠自信地提出和解決複雜的數據問題，構建有效的機器學習流程。本書將為您提供將抽象概念和原始統計數據轉化為可行洞察力的工具。

從數據清理和準備開始，您將探索有效的數據挖掘策略和技巧，然後全面了解數據科學拼圖的每一個部分如何相互配合。在整本書中，您將發現統計模型，可以控制和處理最密集或最稀疏的數據集，並學習如何創建強大的可視化圖表，傳達數據中隱藏的故事。

本版專注於應用，涵蓋了高級的轉移學習和用於自然語言處理和視覺任務的預訓練模型。您將掌握減輕數據和模型中的算法偏見以及解決模型和數據漂移的高級技術。最後，您將探索中級數據治理，包括數據來源、隱私和刪除請求處理。

通過閱讀本書，您將學習計算數學和統計學的基礎知識，同時掌握現代機器學習和大型預訓練模型（如GPT和BERT）的細節。

您將學到什麼：
- 通過實際示例掌握數據科學的基本步驟。
- 通過高級統計和機器學習，彌合數學和編程之間的差距。
- 利用概率、微積分和模型實現有效的數據控制。
- 探索具有轉換性的現代機器學習和大型語言模型。
- 使用有影響力的指標和MLOps評估機器學習的成功。
- 創建引人入勝的視覺效果，傳達可行的洞察力。
- 量化和減輕數據和機器學習模型中的偏見。

本書適合對數據科學有興趣的初學者，無論您是否具備基本的數學技能並希望在數據科學領域應用它們，或者您在編程方面表現出色但缺乏必要的數學基礎，您都會發現本書很有用。熟悉Python編程將進一步提升您的學習體驗。