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Prove your skills as a Data Scientist with the CompTIA(R) DataX Study Guide The
CompTIA(R)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.
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- 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
Sommario
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
Info autore
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.
