Data Analytics: Systems Engineering - Cybersecurity - Project Management

147 Pages·2021·2.128 MB·English

by  Christopher Greco

Data Analytics
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Data Analytics
Systems Engineering • Cybersecurity • Project Management
Christopher Greco
Mercury Learning and Information
Dulles, Virginia
Boston, Massachusetts
New Delhi
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C. Greco. Data Analytics: Systems Engineering • Cybersecurity • Project Management
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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
vi • Data Analytics
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
Contents • vii
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
viii • Data Analytics
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 R eal-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
Contents • ix
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.1 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