The breadth of problems that can be solved with data science is astonishing, and this book provides the required tools and skills to a broad audience. The necessary background in computer science, mathematics, and statistics is provided in an approachable manner.
The breadth of problems that can be solved with data science is astonishing, and this book provides the required tools and skills to a broad audience. The necessary background in computer science, mathematics, and statistics is provided in an approachable manner.
Adam P. Tashman has been working in data science for over twenty years. He is Associate Professor of Data Science at the University of Virginia School of Data Science. He is currently Director of the Capstone program, and he was formerly Director of the Online Master's of Data Science program. He was the School of Data Science Capital One Fellow for the 2023-2024 academic year. Dr. Tashman won multiple awards from Amazon Web Services, where he advised education and government technology companies on best practices in machine learning and artificial intelligence. Dr. Tashman lives in Charlottesville, VA with his wonderful wife Elle and daughter Callie.
Inhaltsangabe
1. Introduction 2. Communicating Effectively and Earning Trust 3. Data Science Project Planning 4. An Overview of Data 5. Computing Preliminaries and Setup 6. Data Processing 7. Data Storage and Retrieval 8. Mathematics Preliminaries 9. Statistics Preliminaries 10. Data Transformation 11. Exploratory Data Analysis 12. An Overview of Machine Learning 13. Modeling with Linear Regression 14. Classification with Logistic Regression 15. Clustering with K-Means 16. Elements of Reproducible Data Science 17. Model Risk 18. Next Steps Symbols
1. Introduction 2. Communicating Effectively and Earning Trust 3. Data Science Project Planning 4. An Overview of Data 5. Computing Preliminaries and Setup 6. Data Processing 7. Data Storage and Retrieval 8. Mathematics Preliminaries 9. Statistics Preliminaries 10. Data Transformation 11. Exploratory Data Analysis 12. An Overview of Machine Learning 13. Modeling with Linear Regression 14. Classification with Logistic Regression 15. Clustering with K-Means 16. Elements of Reproducible Data Science 17. Model Risk 18. Next Steps Symbols
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