CompTIA Data+ Certification Training Course Outline
Module 1: Identifying Basic Concepts of Data Schemas
- Identify the Key Differences Between Relational and Non-Relational Databases
- Relational Databases
- Non-Relational Databases
Lab: Navigating and Understanding Database Design
- Identify the Way We Use Tables, Primary Keys, and Normalization
- Normalization
- Normalizing Data
- Relationships in Data
- Types of Relationships
- Referential Integrity
- Denormalization
Module 2: Understanding Different Data Systems
- Describe Types of Data Processing and Storage Systems
- Types of Data Processing
- Source Systems
- Data Warehouses and Data Marts
- Schemas Used in Data Warehousing
- Fact Table
- Dimension Table
- Star Schema
- Snowflake Schema
- Data Lakes and Lakehouses
- Explain How Data Changes
- Overview of Slowly Changing Dimensions
- Impact of Slowly Changing Dimensions
Module 3: Understanding Data Types and Characteristics of Data
- Understand Types of Data
- Quantitative Data
- Qualitative Data
- Why the Data Types Matter?
- Break Down the Field Data Types
- Introduction to Field Data Types
- Text/Alphanumeric Field Data Types
- Date Data Type
- Number Date Types
- Currency Data Type
- Boolean Data Type
- Data Type Conversion
Lab: Understanding Data Types and Conversion
Lab: Understanding Data Structure and Types and Using Basic Statements
Module 4: Comparing and Contrasting Different Data Structures, Formats, and Markup Languages
- Differentiate Between Structured Data and Unstructured Data
- Structured Data
- Unstructured Data
- Recognize Different File Formats
- Delimited Files
- Why We Use Delimited Files?
- Flat Files
- File Extensions
Lab: Working with Different File Formats
- Understand the Different Code Languages Used for Data
- Structured Query Language (SQL)
- Structured Hyper Text Markup Language (HTML)
- Extensible Markup Language (XML)
- JavaScript Object Notation (JSON)
Module 5: Explaining Data Integration and Collection Methods
- Understand the Processes of Extracting, Transforming, and Loading Data
- Extracting Data
- Transforming Data
- Loading Data
- Full Load and Delta Load
- Extract, Load, Transform (ELT)
- Explain API/Web Scraping and Other Collection Methods
- Application Programming Interface (API)
- Web Services
- Web Scraping
- Machine Data
- Collect and Use Public Data
- Overview of Public and Publicly-Available Data
- Finding Public and Publicly-Available Data
Lab: Using Public Data
- Use and Collect Survey Data
- Considerations for Using Surveys
- Question Design
- Types of Survey Answers
Module 6: Identifying Common Reasons for Data Cleansing and Profiling Datasets
- Learn to Profile Data
- Steps of Data Profiling
- Data Profiling Tools and Techniques
Lab: Profiling Data Sets
- Address Redundant and Duplicated Data
- Redundant Data
- Duplicated Data
- Unnecessary Fields
Lab: Addressing Redundant and Duplicated Data
- Work with Missing Values
- Causes of Null Values
- Filtering Null Values
- Replacing Missing Values
Lab: Addressing Missing Values
- Address Invalid Data
- Identifying Invalid Data
- Removing Invalid Data
- Replacing Invalid Data with Valid Data
- Convert Data to Meet Specifications
- Data That Does Not Meet Specifications
- Converting Data Types
Lab: Preparing Data for Use
Module 7: Executing Different Data Manipulation Techniques
- Recode Data and Derived Variables
- Recoding Numerical and Categorical Data
- Derived Variables
- Imputing Values
- Reduction in Data Sets
- Masking Values
Lab: Recoding Data
- Transpose and Append Data
- Transposing Data
- Appending Data
- Query Data
- Querying Data
- Types of Joins
Lab: Working with Queries and Join Types
Module 8: Explain Common Techniques for Data Manipulation and Optimization
- Use Functions to Manipulate Data
- Text Functions
- Text Functions - Left, Right, Mid
- Text Functions - Upper, Lower, and Proper
- Combining Data Fields
- Parsing Strings for Information
- Date Functions
- Logical Functions and Conditional Formatting
- Aggregation and the Basic Types of Aggregate Functions
- System Functions
- Use Common Techniques for Query Optimization
- Filtering Data
- Parameterization
- Indexing Data
- Temporary Tables
- Sub Querying and Subsets of Information
- Query Execution Plan
Lab: Building Queries and Transforming Data
Module 9: Applying Descriptive Statistical Methods
- Use Measures of Central Tendency
- Measures of Central Tendency Overview
Lab: Using the Measures of Central Tendency
- Use Measures of Dispersion
- Overview of the Measures of Dispersion
- Range of Data
- Standard Deviation
- Z-Scores
- Distribution of a Data Set
Lab: Using the Measures of Variability
- Use Frequencies and Percentages
- Frequency
- Percentage Difference
- Percentage Change
Module 10: Describing Key Analysis Techniques
- Get Started with Analysis
- Research Questions
- Sample Research Questions
- Data Sources and Collection Methods
- Observations
- Recognize Types of Analysis
- Exploratory Analysis
- Performance Analysis
- Gap Analysis
- Trend Analysis
- Link Analysis
Module 11: Understanding the Use of Different Statistical Methods
- Understand the Importance of Statistical Tests
- Confidence Intervals
- T-Tests and P-Values
- Break Down the Hypothesis Test
- Null Hypothesis
- Understanding the Results of Hypothesis Testing
- Understand Tests and Methods to Determine Relationships Between Variables
- Chi-Square
- Chi-Square Tests
- Simple Linear Regression
- Correlation
- Use Excel to Apply Statistical Methods
Lab: Analysing Data
Module 12: Using the Appropriate Type of Visualization
- Use Basic Visuals
- Pie Chart
- Treemaps
- Column and Bar Charts
- Line Graphs
Lab: Building Basic Visuals to Make Visual Impact
- Build Advanced Visuals
- Stacked Column/Bar Charts
- Line Graphs with Multiple Lines
- Combination Charts
- Scatter Plots
- Bubble Charts
- Histograms
- Waterfall Charts
- Build Maps with Geographical Data
- Preparing Geo Fields for Mapping
- Geographic Maps
Lab: Building Maps with Geographical Data
- Use Visuals to Tell a Story
- Heat Maps
- Word Clouds
- Infographics
Lab: Using Visuals to Tell a Story
Module 13: Expressing Business Requirements in a Report Format
- Consider Audience Needs When Developing a Report
- Describe Data Source Considerations for Reporting
- Documenting the Source Data
- Determining Access to Data
- Developing Views of the Data
- Data Fields and Attributes
- Describe Considerations for Delivering Reports and Dashboards
- Determining How Visuals Will Be Viewed
- Determining How Data Will Be Delivered
- Frequency of Reporting
- Recurring Reports
- Develop Reports or Dashboards
- Visualisation Layouts
- Mockup and Wireframing for Design
- Types of Visuals
- Types of Dashboard Navigation
- Understand Ways to Sort and Filter Data
- Sorting Data
- Filter Methods for Visuals
- Filtering by Date Ranges
Lab: Filtering Data
Module 14: Designing Components for Reports and Dashboards
- Design Elements for Reports/Dashboards
- Branding Guidelines
- Appropriate Color Schemes
- Appropriate Fonts and Layout
- Naming Conventions
Lab: Designing Elements for Dashboards
- Utilize Standard Elements
- Standard Information and Formatting Elements for Reports
- Other Special Fields
- Watermarks
- Important Dates
- Create a Narrative and Other Written Elements
- Narrative
- Instructions for Using the Report/Dashboard
- Other Supporting Materials
- Understand Deployment Considerations
- Techniques for Dashboard Optimisation
- Expand and Collapse Options for Information
- Drill Through
- Tooltips
- Other Considerations
- Deploy to Production
Module 15: Distinguish Different Report Types
- Understand How Updates and Timing Affect Reporting
- Static Vs Dynamic Reports
- Point-in-Time Reporting
- Real-Time Reporting
- Differentiate Between Types of Reports
- Operational and Compliance Reports
- Tactical and Research-Driven Reporting
- Ad-Hoc Reporting
- Self-Service Reporting
Lab: Building an Ad Hoc Report
Lab: Visualising Data
Module 16: Summarizing the Importance of Data Governance
- Define Data Governance
- Lifecycle of Data
- Roles Within a Data Governance Team
- Jurisdiction Requirements
- Regulations and Compliance
- Data Classifications
- Understanding Access Requirements and Policies
- Data Use Agreements
- Release Approvals
- Data Retention and Destruction Policies
- Understand Security Requirements
- Data Processing
- Data Transmission
- Data Encryption
- De-Identification and Masking of Data
- Data Breaches
- Data Access
- Saving Data Files and Storage Types
Lab: Building Basic Visuals to Make Visual Impact
- Understanding Entity Relationship Requirements
- Entity Relationship Models
- Record Linkage Restrictions
- Data Constraints
Module 17: Applying Quality Control to Data
- Describe Characteristics, Rules, and Metrics of Data Quality
- Reasons to Check Data Quality
- Understanding Quality
- Rules and Metrics for Data Quality
- Identify Reasons to Quality Check Data and Methods of Data Validation
- Data Validation Methods
- Automated Validation
- Data Verification Methods
Module 18: Explaining Master Data Management
- Explain the Basics of Master Data Management
- Master Data Management
- Benefits of Master Data Management
- Reasons for Master Data Management
- Master Data Management Vs Data Warehouse
- Describe Master Data Management Processes
- Consolidation of Multiple Data Fields
- Field Standardization
- Data Dictionary