Data Analytics

Systems Engineering - Cybersecurity - Project Management

Christopher Greco

• Publisher: Mercury Learning and Information
• Year: 2021
• ISBN:
  9781683926467
• Topic: Computer Science
• Subtopic: Data Mining
• Index: Computer Science

Data analytics is creeping into the lexicon of our daily language. This book gives the reader a perspective as to the overall data analytics skill set, starting with a primer on statistics, and works toward the application of those methods. There are a variety of formulas and algorithms used in the data analytics process. These formulas can be plugged into whatever software application the reader uses to obtain the answer they need. There are several demonstrations of this process to provide straightforward instruction as to how to bring data analytics skills to your critical thinking. This book presents a variety of methods and techniques, as well as case studies, to enrich the knowledge of data analytics for project managers, systems engineers, and cybersecurity professionals. It separates the case studies so that each profession can practice some straightforward data analytics specific to their fields. The main purpose of this text is to refresh the statistical knowledgenecessary to build models for data analytics. Along with that, this book encompasses the analytics thinking that is essential to all three professions. FEATURES:

• Provides straightforward instruction on data analytics methods
• Includes methods, techniques, and case studies for project managers, systems engineers, and cybersecurity professionals
• Refreshes the statistical knowledgeneeded to bring data analytics into your skillset and decision-making
• Focuses on getting readers up to speed quickly and efficiently to be able to see the impact of data analytics and analytical thinking

Table of contents

• Cover
• Title
• Copyright
• Contents
• Preface
• Acknowledgments
• 1. Introduction To Statistics For Data Analysts
• 2. What Is Data?
• 3. Statistics Review – Measures of The Central Tendency
• 4. Probability Primer
• 5. Occam’s Razor and Data Analytics
• 6. Data Analysis Tools
• 7. Effect Size
• 8. Analysis Process Methods
• 9. Data Analytics Thinking
• 10. Where’s The Data?
• 11. Data Presentation
• 12. Geospatial Data Analytics
• 13. Additional Data Analytics Methods
• 14. Summary
• 15. Case Studies
• Appendix: Recommended Solutions for Case Studies
• References
• Index