Comptia Datax Study Guide

Prove your skills as a Data Scientist with the CompTIA® DataX Study Guide The CompTIA®DataX Study Guide is your one-stop resource for complete coverage of the DY0-001 exam. This Sybex Study Guide covers all the DY0-001 objectives. Prepare for the exam smarter and faster with Sybex thanks to efficient and accurate content, including assessment test that validate and measure exam readiness, real-world examples and scenarios, practical exercises, and challenging chapter review questions. Reinforce and remember what you've learned with the intuitive Sybex online learning environment and test bank, accessible across multiple devices. Prepare like a pro for the CompTIA DataX exam with Sybex. Coverage of 100% of all exam objectives in this Study Guide means you'll be ready: - To understand data science operations and processes - To implement key data science best practices- To apply mathematical and statistical models appropriately - To decide how to clean and process data effectively and efficiently- To apply concepts from statistical modeling, linear algebra, and calculus - To apply machine-learning models and understand deep learning concepts- To make justified model recommendations ABOUT THE COMPTIA DATAX CERTIFICATION CompTIA DataX certification validates your understanding of data and your ability to leverage data and artificial intelligence to make predictions and communicate those predictions to stakeholders. Interactive learning environment Take your exam prep to the next level with Sybex's superior interactive online study tools. To access our learning environment, simply visit www.wiley.com/ go/sybextestprep, register your book to receive your unique PIN, and instantly gain one year of FREE access after activation to: - Interactive test bank with 2 practice exams to help you identify areas where further review is needed. Get more than 90% of the answers correct, and you're ready to take the certification exam. - Over 100 electronic flashcards to reinforce learning and last-minute prep before the exam

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