Develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with an exclusive focus on the popular data science tool Python. The book includes many examples of real-life applications, with practice ranging from small to big data.
Develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with an exclusive focus on the popular data science tool Python. The book includes many examples of real-life applications, with practice ranging from small to big data.
Chirag Shah is Professor of Information and Computer Science at University of Washington (UW) in Seattle. He is the Founding Director for InfoSeeking Lab and Founding Co-Director of the Center for Responsibility in AI Systems & Experiences (RAISE). His research focuses on building, auditing, and correcting intelligent information access systems. Dr. Shah is a Distinguished Member of ACM as well as ASIS&T, and a Senior Member of IEEE. He has published nearly 200 peer-reviewed articles and authored several books, including textbooks on data science and machine learning. He regularly engages with industrial research labs at Amazon, ByteDance, Microsoft Research, and Spotify.
Inhaltsangabe
Part I. Conceptual Introductions: 1. Introduction 2. Data Part II. Tools for Data Science: 3. Techniques 4. Python 5. Python for Statistical Analysis 6. Cloud Computing Part III. Machine Learning for Data Science: 7. Machine Learning Introduction and Regression 8. Supervised Learning 9. Unsupervised Learning Part IV. Applications, Evaluations, and Methods: 10. Data Collection, Experimentation, and Evaluation 11. Hands-On with Solving Data Problems.
Part I. Conceptual Introductions: 1. Introduction 2. Data Part II. Tools for Data Science: 3. Techniques 4. Python 5. Python for Statistical Analysis 6. Cloud Computing Part III. Machine Learning for Data Science: 7. Machine Learning Introduction and Regression 8. Supervised Learning 9. Unsupervised Learning Part IV. Applications, Evaluations, and Methods: 10. Data Collection, Experimentation, and Evaluation 11. Hands-On with Solving Data Problems.
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