CompTIA Data+

Module 1: Identifying Basic Concepts of Data Schemas
In this module, you will learn how to:

• Identify Relational and Non-Relational Databases
• Understand the Way We Use Tables, Primary Keys, and Normalization

Module 2: Understanding Different Data Systems
In this module, you will learn how to:

• Describe Types of Data Processing and Storage Systems
• Explain How Data Changes

Module 3: Understanding Types and Characteristics of Data
In this module, you will learn how to:

• Understand Types of Data
• Break Down the Field Data Types

Module 4: Comparing and Contrasting Different Data Structures, Formats, and Markup Languages
In this module, you will learn how to:

• Differentiate between Structured Data and Unstructured Data
• Recognize Different File Formats
• Understand the Different Code Languages Used for Data

Module 5: Explaining Data Integration and Collection Methods
In this module, you will learn how to:

• Understand the Processes of Extracting, Transforming, and Loading Data
• Explain API/Web Scraping and Other Collection Methods
• Collect and Use Public and Publicly Available Data
• Use and Collect Survey Data

Module 6: Identifying Common Reasons for Cleansing and Profiling Data
In this module, you will learn how to:

• Learn to Profile Data
• Address Redundant, Duplicated, and Unnecessary Data
• Work with Missing Values
• Address Invalid Data
• Convert Data to Meet Specifications

Module 7: Executing Different Data Manipulation Techniques
In this module, you will learn how to:

• Manipulate Field Data and Create Variables
• Transpose and Append Data
• Query Data

Module 8: Explaining Common Techniques for Data Manipulation and Optimization
In this module, you will learn how to:

• Use Functions to Manipulate Data
• Use Common Techniques for Query Optimization

Module 9: Applying Descriptive Statistical Methods
In this module, you will learn how to:

• Use Measures of Central Tendency
• Use Measures of Dispersion
• Use Frequency and Percentages

Module 10: Describing Key Analysis Techniques
In this module, you will learn how to:

• Get Started with Analysis
• Recognize Types of Analysis

Module 11: Understanding the Use of Different Statistical Methods
In this module, you will learn how to:

• Understand the Importance of Statistical Tests
• Break Down the Hypothesis Test
• Understand Tests and Methods to Determine Relationships Between Variables

Module 12: Using the Appropriate Type of Visualization
In this module, you will learn how to:

• Use Basic Visuals
• Build Advanced Visuals
• Build Maps with Geographical Data
• Use Visuals to Tell a Story

Module 13: Expressing Business Requirements in a Report Format
In this module, you will learn how to:

• Consider Audience Needs When Developing a Report
• Describe Data Source Considerations for Reporting
• Describe Considerations for Delivering Reports and Dashboards
• Develop Reports or Dashboards
• Understand Ways to Sort and Filter Data

Module 14: Designing Components for Reports and Dashboards
In this module, you will learn how to:

• Design Elements for Reports and Dashboards
• Utilize Standard Elements
• Creating a Narrative and Other Written Elements
• Understand Deployment Considerations

Module 15: Distinguishing Different Report Types
In this module, you will learn how to:

• Understand How Updates and Timing Affect Reporting
• Differentiate Between Types of Reports

Module 16: Summarizing the Importance of Data Governance
In this module, you will learn how to:

• Define Data Governance
• Understand Access Requirements and Policies
• Understand Security Requirements
• Understand Entity Relationship Requirements

Module 17: Applying Quality Control to Data
In this module, you will learn how to:

• Describe Characteristics, Rules, and Metrics of Data Quality
• Identify Reasons to Quality Check Data and Methods of Data Validation

Module 18: Explaining Master Data Management Concepts
In this module, you will learn how to:

• Explain the Basics of Master Data Management
• Describe Master Data Management Processes

• Identify Data Concepts and Environments important in analytics.
• Execute techniques in Data Mining, Data Mining, and Visualization.
• Summarize the importance of Data Governance, Quality, and Controls.
• Continue learning and face new challenges with after-course one-on-one instructor coaching.

• Data Analyst
• Clinical Analyst
• Reporting Analyst
• Marketing Analyst
• Business Data Analyst
• Operations Analyst
• Business Intelligence Analyst