
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
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.
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Information
Table of contents
- Cover
- Table of Contents
- Title Page
- Copyright
- Dedication
- Acknowledgments
- About the Author
- About the Technical Editor
- Introduction
- Chapter 1: What Is Data Science?
- Chapter 2: Mathematics and Statistical Methods
- Chapter 3: Data Collection and Storage
- Chapter 4: Data Exploration and Analysis
- Chapter 5: Data Processing and Preparation
- Chapter 6: Modeling and Evaluation
- Chapter 7: Model Validation and Deployment
- Chapter 8: Unsupervised Machine Learning
- Chapter 9: Supervised Machine Learning
- Chapter 10: Neural Networks and Deep Learning
- Chapter 11: Natural Language Processing
- Chapter 12: Specialized Applications of Data Science
- Appendix: Answers to Review Questions
- Index
- End User License Agreement