CompTIA DataX Study Guide

Demonstrate your Data Science skills by earning the brand-new CompTIA DataX credential In CompTIA DataX Study Guide: Exam DY0-001, data scientist and analytics professor, Fred Nwanganga, delivers a practical, hands-on guide to establishing your credentials as a data science practitioner and succeeding on the CompTIA DataX certification exam. In this book, you'll explore all the domains covered by the new credential, which include key concepts in mathematics and statistics; techniques for modeling, analysis and evaluating outcomes; foundations of machine learning; data science operations and processes; and specialized applications of data science. This up-to-date Study Guide walks you through the new, advanced-level data science certification offered by CompTIA and includes hundreds of practice questions and electronic flashcards that help you to retain and remember the knowledge you need to succeed on the exam and at your next (or current) professional data science role. You'll find: - Chapter review questions that validate and measure your readiness for the challenging certification exam - Complimentary access to the intuitive Sybex online learning environment, complete with practice questions and a glossary of frequently used industry terminology - Material you need to learn and shore up job-critical skills, like data processing and cleaning, machine learning model-selection, and foundational math and modeling concepts Perfect for aspiring and current data science professionals, CompTIA DataX Study Guide is a must-have resource for anyone preparing for the DataX certification exam (DY0-001) and seeking a better, more reliable, and faster way to succeed on the test.

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