Data Analytics: Systems Engineering - Cybersecurity - Project Management

✍ Scribed by Christopher Greco

• Publisher: Mercury Learning and Information
• Year: 2021
• Tongue: English
• Leaves: 147
• Category: Library

✦ Synopsis

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 page
License page
Half Title page
Copyright page
Contents
Preface
Acknowledgments
Chapter 1: Introduction toStatistics for DataAnalysts
1.1 Objectives
1.2 The Three Professions
Chapter 2: What is Data?
2.1 Data Types
2.1.1 Quantitative values
2.1.2 Qualitative values
2.1.3 Application of Each Type of Data
Chapter 3: Statistics Review –Measures of theCentral Tendency
3.1 Mean
3.1.0 Averaging with the PERT Method
3.1.1 Geometric Mean
3.2 Median
3.3 Mode
3.4 Data Skew
3.4.1 Kurtosis
3.5 Measures of Variation
3.5.1 Variance
3.5.2 Standard Deviation
3.5.2.1 Real-World Use of the Standard Deviation
3.6 Standard Normal Curve vs. Normal Curve
3.7 Other Measures of Variation
3.7.1 Mean Absolute Deviation
3.7.2 Median Absolute Deviation
3.7.3 Still More Tests for Variation
3.7.3.1 Range
3.7.3.2 Inter-Quartile Range (IQR)
3.7.3.3 Percentile
3.7.4 Five Number Summary
Chapter 4: Probability Primer
4.1 Addition Method in Probability
4.2 Multiplication Property of Probability
4.3 Bayesian Probability
Chapter 5: Occam’s Razor andData Analytics
5.1 Data Origination
Chapter 6: Data Analysis Tools
6.1 Microsoft Excel
6.2 R Stats
6.3 Open Office
6.4 Minitab
6.5 Tableau, SPSS, QLIK, and others
6.6 Geospatial Statistical Systems
6.6.1 ARCGIS
6.6.2 QGIS
Chapter 7: Effect Size
7.1 Correlation
7.1.1 Correlation does not mean causation, but …
Chapter 8: Analysis ProcessMethods
8.1 CRISP-DM Method
8.1.1 Understand the Organization
8.1.2 Understanding the Data
8.1.3 Preparing the Data
8.1.4 Analyze and Interpret the Data
8.1.5 Evaluate the Analysis
8.1.6 Communicate and Deploy the Results
8.2 Alternative Method
8.2.1 Framing the Question
8.2.2 Understanding Data
8.2.3 Choose a Method
8.2.4 Calculate the Statistics
8.2.5 Interpret the Statistics
8.2.6 Test the Significance of the Statistics
8.2.7 Question the Results
Chapter 9: Data Analytics Thinking
9.1 Elements of Data Analytic Thinking
9.1.1 Data Structure
9.1.2 Analysis Elements Inside Data
9.1.3 Analysis Elements Outside Data
9.2 There is a “Why” in Analysis
9.2.1 The “V’s” in Data
9.2.1.1 Data Velocity
9.2.1.2 Data Variety
9.2.1.3 Data Volume
9.2.1.4 Data Vulnerability
9.3 Risk
9.3.1 Probability of Risk
9.3.2 Risk Impact
9.3.3 The Risk Chart
Chapter 10: Where’s the Data?
10.1 Data Locations
10.2 How Much Data?
10.3 Sampling
10.3.1 Random Sampling
10.3.2 Systematic Sampling
10.3.3 Sampling Bias
10.3.3.1 Mitigating Data Bias
10.3.4 Determinism
10.3.4.1 Lift
10.3.4.2 Leverage
10.3.4.3 Support
10.3.4.4 Strength
Chapter 11: Data Presentation
11.1 The Good, The Bad, and The OMG
11.2 Real-World Example from a Project ManagementPerspective
Chapter 12: Geospatial DataAnalytics
12.1 Geospatial Mean Center
12.1.1 Real-World Application of Geospatial Mean
12.2 Standard Distance
12.3 Standard Deviational Ellipse
12.4 Geary’s C
Chapter 13: Additional DataAnalytic Methods
13.1 Entropy
13.2 Effect Size, Part 2
13.3 Modeling and Simulation
13.3.1 Model Type
13.3.2 Simulation
Chapter 14: Summary
Chapter 15: Case Studies
15.1 Case Study Scenario
15.2 Case Study: Description of Data
15.3 Case Study: Normal Curve
15.4 Case Study: Variation Measures
15.5 Case Study: Probability
15.6 Case Study: Occam’s Razor
Appendix: Recommended Solutionsfor Case Studies
Introduction
A.1 Recommended Approach for Case Study 15.2
A.2 Recommended Approach for Case Study 15.3
A.3 Recommended Approach for Case Study 15.4
A.4 Recommended Approach for Case Study 15.5
A.5 Recommended Approach for Case Study 15.6
References
INDEX