ABOUT THE AUTHOR FRED NWANGANGA is a technology professional and professor in the IT, Analytics, and Operations Department within the University of Notre Dame – Mendoza College of Business. He teaches undergraduate and graduate courses in Python for Data Analytics, Machine Learning, and Unstructured Data Analytics. He has over 20 years of experience in technology management and analytics. He is the author of several LinkedIn Learning machine learning courses and the founder of the Early Bridges to Data Science Program in the Notre Dame Lucy Family Institute for Data & Society.

• Introduction xxiiiChapter 1 What Is Data Science? 1Chapter 2 Mathematics and Statistical Methods 25Chapter 3 Data Collection and Storage 63Chapter 4 Data Exploration and Analysis 97Chapter 5 Data Processing and Preparation 131Chapter 6 Modeling and Evaluation 167Chapter 7 Model Validation and Deployment 195Chapter 8 Unsupervised Machine Learning 225Chapter 9 Supervised Machine Learning 249Chapter 10 Neural Networks and Deep Learning 271Chapter 11 Natural Language Processing 293Chapter 12 Specialized Applications of Data Science 315Appendix Answers to Review Questions 337Chapter 1: What Is Data Science? 338Chapter 2: Mathematics and Statistical Methods 339Chapter 3: Data Collection and Storage 341Chapter 4: Data Exploration and Analysis 343Chapter 5: Data Processing and Preparation 345Chapter 6: Modeling and Evaluation 346Chapter 7: Model Validation and Deployment 347Chapter 8: Unsupervised Machine Learning 349Chapter 9: Supervised Machine Learning 350Chapter 10: Neural Networks and Deep Learning 352Chapter 11: Natural Language Processing 353Chapter 12: Specialized Applications of Data Science 355Index 357