Data Analytics: Systems Engineering - Cybersecurity - Project Management

by Christopher Greco

Overview

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

Product Details

ISBN-13:	9781683926481
Publisher:	Mercury Learning and Information
Publication date:	02/08/2021
Pages:	148
Product dimensions:	7.00(w) x 9.00(h) x (d)

About the Author

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.

Table of Contents

Preface xi

Acknowledgments xiii

1 Introduction to Statistics for Data Analysts 1-4

1.1 Objectives 2

1.2 The Three Professions 3

2 What is Data? 5-10

2.1 Data Types 5

2.1.1 Quantitative values 6

2.1.2 Qualitative values 7

2.1.3 Application of Each Type of Data 8

3 Statistics Review - Measures of the Central Tendency 11-32

3.1 Mean 12

3.1.0 Averaging with the PERT Method 13

3.1.1 Geometric Mean 14

3.2 Median 15

3.3 Mode 16

3.4 Data Skew 18

3.4.1 Kurtosis 19

3.5 Measures of Variation 19

3.5.1 Variance 20

3.5.2 Standard Deviation 22

3.5.2.1 Real-World Use of the Standard Deviation 22

3.6 Standard Normal Curve vs. Normal Curve 23

3.7 Other Measures of Variation 25

3.7.1 Mean Absolute Deviation 25

3.7.2 Median Absolute Deviation 26

3.7.3 Still More Tests for Variation 28

3.7.3.1 Range 28

3.7.3.2 Inter-Quartile Range (IQR) 28

3.7.3.3 Percentile 30

3.7.4 Five Number Summary 31

4 Probability Primer 33-38

4.1 Addition Method in Probability 34

4.2 Multiplication Property of Probability 35

4.3 Bayesian Probability 36

5 Occam's Razor and Data Analytics 39-42

5.1 Data Origination 40

6 Data Analysis Tools 43-48

6.1 Microsoft Excel 43

6.2 R Stats 44

6.3 Open Office 44

6.4 Minitab 44

6.5 Tableau, SPSS, QLIK, and others 45

6.6 Geospatial Statistical Systems 45

6.6.1 ARCGIS 46

6.6.2 QGIS 47

7 Effect Size 49-52

7.1 Correlation 49

7.1.1 Correlation does not mean causation, but 51

8 Analysis Process Methods 53-62

8.1 CRISP-DM Method 53

8.1.1 Understand the Organization 54

8.1.2 Understanding the Data 55

8.1.3 Preparing the Data 55

8.1.4 Analyze and Interpret the Data 56

8.1.5 Evaluate the Analysis 57

8.1.6 Communicate and Deploy the Results 57

8.2 Alternative Method 58

8.2.1 Framing the Question 59

8.2.2 Understanding Data 59

8.2.3 Choose a Method 60

8.2.4 Calculate the Statistics 61

8.2.5 Interpret the Statistics 61

8.2.6 Test the Significance of the Statistics 62

8.2.7 Question the Results 62

9 Data Analytics Thinking 63-76

9.1 Elements of Data Analytic Thinking 64

9.1.1 Data Structure 64

9.1.2 Analysis Elements Inside Data 64

9.1.3 Analysis Elements Outside Data 65

9.2 There is a "Why" in Analysis 66

9.2.1 The "V's" in Data 67

9.2.1.1 Data Velocity 67

9.2.1.2 Data Variety 68

9.2.1.3 Data Volume 68

9.2.1.4 Data Vulnerability 70

9.3 Risk 71

9.3.1 Probability of Risk 73

9.3.2 Risk Impact 74

9.3.3 The Risk Chart 75

10 Where's the Data? 77-88

10.1 Data Locations 77

10.2 How Much Data? 81

10.3 Sampling 81

10.3.1 Random Sampling 82

10.3.2 Systematic Sampling 83

10.3.3 Sampling Bias 83

10.3.3.1 Mitigating Data Bias 84

10.3.4 Determinism 85

10.3.4.1 Lift 85

10.3.4.2 Leverage 86

10.3.4.3 Support 86

10.3.4.4 Strength 87

11 Data Presentation 89-94

11.1 The Good, the Bad, and the OMG 89

11.2 Real-World Example from a Project Management Perspective 92

12 Geospatial Data Analytics 95-102

12.1 Geospatial Mean Center 95

12.1.1 Real-World Application of Geospatial Mean 97

12.2 Standard Distance 99

12.3 Standard Deviational Ellipse 99

12.4 Geary's C 100

13 Additional Data Analytics Methods 103-110

13.1 Entropy 103

13.2 Effect Size, Part 2 105

13.3 Modeling and Simulation 106

13.3.1 Model Type 106

13.3.2 Simulation 109

14 Summary 111-112

15 Case Studies 113-122

15.1 Case Study Scenario 113

15.2 Case Study: Description of Data 114

15.3 Case Study: Normal Curve 116

15.4 Case Study: Variation Measures 117

15.5 Case Study: Probability 120

15.6 Case Study: Occam's Razor 121

Appendix Recommended Solutions for Case Studies 123-126

A Introduction 123

A.l Recommended Approach for Case Study 15.2 123

A.2 Recommended Approach for Case Study 15.3 124

A.3 Recommended Approach for Case Study 15.4 124

A.4 Recommended Approach for Case Study 15.5 125

A.5 Recommended Approach for Case Study 15.6 126

References 127-128

Index 129-132