Data Analytics
Systems Engineering - Cybersecurity - Project Management
- Publisher
Mercury Learning & Information - Published
8th February - ISBN 9781683926481
- Language English
- Pages 132 pp.
- Size 7" x 9"
- Publisher
Mercury Learning & Information - Published
14th January - ISBN 9781683926467
- Language English
- Pages 132 pp.
- Size 7" x 9"
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- Publisher
Mercury Learning & Information - Published
14th January - ISBN 9781683926474
- Language English
- Pages 132 pp.
- Size 7" x 9"
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 knowledge
necessary 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 knowledge needed 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
1: Introduction to Statistics for Data Analysts
2: What Is Data?
3: Statistics Review
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
Christopher Greco
Christopher Greco is a COMPTIA Certified Technical Trainer and Microsoft Certified Systems Engineer with numerous years of industry experience in the areas of data analysis, cybersecurity, and IT instruction and training.