Data Analytics: Systems Engineering - Cybersecurity - Project Management

Description

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 methodsIncludes methods, techniques, and case studies for project managers, systems engineers, and cybersecurity professionalsRefreshes the statistical knowledgeneeded to bring data analytics into your skillset and decision-makingFocuses on getting readers up to speed quickly and efficiently to be able to see the impact of data analytics and analytical thinking 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