Table of Contents

  1. Introduction

  2. CHAPTER 1:
    Understanding Databases and Data Warehouses

    1. Databases and Database Management Systems

      1. Database Management System (DBMS)

      2. Relational Database

      3. Non-relational Database

    2. Data Warehouses and Data Lakes

      1. Data Warehouses

      2. Data Lakes

    3. OLTP and OLAP

      1. Online Transactional Processing (OLTP)

      2. Online Analytical Processing (OLAP)

    4. What Next?

  3. CHAPTER 2:
    Understanding Database Schemas and Dimensions

    1. Schema Concepts

    2. Star and Snowflake Schemas

      1. Star Schema

      2. Snowflake Schema

    3. Slowly Changing Dimensions, Keeping Historical Information, and Keeping Current Information

      1. Keeping Current and Historical Information

      2. Slowly Changing Dimensions

    4. What Next?

  4. CHAPTER 3:
    Data Types and Types of Data

    1. Introduction to Data Types

      1. Storage Sizes of Various Data Types

      2. Character

      3. Integer

      4. Float and Double

      5. Array

      6. String

    2. Comparing and Contrasting Different Data Types

      1. Date

      2. Alphanumeric

      3. Numeric

      4. Text

      5. Currency

    3. Categorical vs. Dimension and Discrete vs. Continuous Data Types

      1. Categorical/Dimension Data Types

      2. Discrete vs. Continuous Data Types

    4. Types of Data: Audio, Video, and Images

      1. Audio

      2. Video

      3. Images

    5. What Next?

  5. CHAPTER 4:
    Understanding Common Data Structures and File Formats

    1. Structured vs. Unstructured Data

      1. Structured vs. Unstructured Data

      2. Structured Data

      3. Rows and Columns

      4. Unstructured Data

      5. Semi-structured Data

      6. Metadata

    2. Data File Formats

      1. Text/Flat File

      2. Tab Delimited File

      3. Comma-Delimited File

      4. JavaScript Object Notation (JSON)

      5. Extensible Markup Language (XML)

      6. Hypertext Markup Language (HTML)

    3. What Next?

  6. CHAPTER 5:
    Understanding Data Acquisition and Monetization

    1. Integration

      1. Data Integration

      2. Extract, Transform, and Load (ETL)

      3. Extract, Load, and Transform (ELT)

      4. Delta Load

      5. Application Programming Interfaces (APIs)/Web Services

    2. Data Collection Methods

      1. Web Scraping

      2. Public Databases

      3. Surveys

      4. Sampling

      5. Observation

    3. What Next?

  7. CHAPTER 6:
    Cleansing and Profiling Data

    1. Profiling and Cleansing Basics

      1. Duplicate Data

      2. Redundant Data

      3. Missing Values

      4. Invalid Data

      5. Non-parametric Data

      6. Data Outliers

      7. Specification Mismatches

      8. Data Type Validation

    2. What Next?

  8. CHAPTER 7:
    Understanding and Executing Data Manipulation

    1. Data Manipulation Techniques

      1. Recoding Data

      2. Derived Variables

      3. Data Merges

      4. Data Blending

      5. Concatenation

      6. Data Appending

      7. Imputation

      8. Data Reduction

      9. Data Transposition

      10. Normalizing Data

      11. Parsing/String Manipulation

      12. Filtering

      13. Sorting

      14. Date Functions

      15. Logical Functions

      16. Aggregate Functions

      17. System Functions

    2. What Next?

  9. CHAPTER 8:
    Understanding Common Techniques for Data Query Optimization and Testing

    1. Query Optimization

      1. Execution Plans

      2. Parameterization

      3. Indexing

      4. Temporary Table in a Query Set

      5. Subsets of Records

    2. What Next?

  10. CHAPTER 9:
    The (Un)Common Data Analytics Tools

    1. Data Analytics Tools

      1. Structured Query Language (SQL)

      2. Python

      3. Microsoft Excel

      4. R

      5. Rapid Miner

      6. IBM Cognos

      7. IBM SPSS Modeler

      8. SAS

      9. Tableau

      10. Power BI

      11. Qlik

      12. MicroStrategy

      13. BusinessObjects

      14. Apex

      15. Datorama

      16. Domo

      17. AWS QuickSight

      18. Stata

      19. Minitab

    2. What Next?

  11. CHAPTER 10:
    Understanding Descriptive and Inferential Statistical Methods

    1. Introduction to Descriptive and Inferential Analysis

      1. Measures of Central Tendency

      2. Measures of Dispersion

      3. Range

      4. Frequencies

      5. Percent Change and Percent Difference

    2. Inferential Statistical Methods

      1. Confidence Intervals

      2. Z-score

      3. t-tests

      4. p-values

      5. Chi-Square Test

      6. Hypothesis Testing

      7. Simple Linear Regression

      8. Correlation

    3. What Next?

  12. CHAPTER 11:
    Exploring Data Analysis and Key Analysis Techniques

    1. Process to Determine Type of Analysis

      1. Determining Data Needs

      2. Review/Refine Business Questions

      3. Data Collection Sources

      4. Gap Analysis

    2. Types of Analysis

      1. Trend Analysis

      2. Performance Analysis

      3. Exploratory Data Analysis

      4. Link Analysis

    3. What Next?

  13. CHAPTER 12:
    Approaching Data Visualization

    1. Business Reports

      1. Report Content

      2. Filters

      3. Views

      4. Date Range

      5. Frequency

      6. Audience for Reports

    2. What Next?

  14. CHAPTER 13:
    Exploring the Different Types of Reports and Dashboards

    1. Report Cover Page and Design Elements

      1. Report Cover Page

      2. Design Elements

    2. Documentation Elements

      1. Version Number

      2. Reference Data Sources and Dates

      3. FAQs and Appendix

    3. Dashboard Considerations, Development, and Delivery Process

      1. Dashboard Considerations

      2. Development Process

      3. Delivery Considerations

    4. What Next?

  15. CHAPTER 14:
    Data-Driven Decision Making: Leveraging Charts, Graphs, and Reports

    1. Types of Data Visualizations

      1. Line Charts

      2. Pie Charts

      3. Bubble Charts

      4. Scatter Plots

      5. Bar Charts

      6. Histograms

      7. Waterfall Charts

      8. Heat Maps

      9. Geographic Maps

      10. Tree Maps

      11. Stacked Charts

      12. Infographics

      13. Word Clouds

    2. Reports

      1. Static Reporting

      2. Dynamic Reports

      3. Ad Hoc/One-Time Reports

      4. Self-Service/On-Demand Reports

      5. Recurring Reports

      6. Tactical/Research Reporting

    3. What Next?

  16. CHAPTER 15:
    Data Governance Concepts: Ensuring a Baseline

    1. Access and Security Requirements

      1. Access Requirements

      2. Security Requirements

    2. Storage Environment Requirements

      1. Shared Drives

      2. Local Storage

      3. Cloud-Based Storage

    3. Use and Entity Relationship Requirements

      1. Use Requirements

      2. Entity Relationship Requirements

    4. Data Classification, Jurisdiction Requirements, and Data Breach Reporting

      1. Data Classification

      2. Jurisdiction Requirements

      3. Data Breach Reporting

    5. What Next?

  17. CHAPTER 16:
    Applying Data Quality Control

    1. Data Quality Dimensions and Circumstances to Check for Quality

      1. Data Quality

      2. Circumstances to Check for Quality

      3. Final Product, Reports, and Dashboards

    2. Data Quality Rules and Metrics, Methods to Validate Quality, and Automated Validation

      1. Data Quality Rules and Metrics

      2. Methods to Validate Data Quality and Automated Validation

      3. Automated Validation

    3. What Next?

  18. CHAPTER 17:
    Understanding Master Data Management (MDM) Concepts

    1. Processes

      1. Consolidation of Multiple Data Fields

      2. Standardization of Data Field Names

      3. Data Dictionary

    2. Circumstances for MDM

      1. Mergers and Acquisitions

      2. Compliance with Policies and Regulations

      3. Streamline Data Access

    3. What Next?

  19. CHAPTER 18:
    Getting Ready for the CompTIA Data+ Exam

    1. Getting Ready for the CompTIA Data+ Exam

    2. Tips for Taking the Real Exam

    3. Beyond the CompTIA Data+ Certification

  20. Index

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