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CompTIA Data+

Course number: CGIDPlus40

CompTIA Data+ is an early-career data analytics certification for professionals tasked with developing and promoting data-driven business decision-making. CompTIA Data+ gives you the confidence to bring data analysis to life.

As the importance for data analytics grows, more job roles are required to set context and better communicate vital business intelligence. Collecting, analyzing, and reporting on data can drive priorities and lead business decision-making.

Course Objectives:

Upon course completion, participants will be able to execute the following tasks:

  • Mining data
  • Manipulating data
  • Visualizing and reporting data
  • Applying basic statistical methods
  • Analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle

CompTIA Data+ from CompTIA


CompTIA® Data+® Exam DA0-001


Post course completion, students can appear for the CompTIA® Data+® Exam DA0-001.

Course Content
Lesson 1: Identifying Basic Concepts of Data Schemas
  • Identify Relational and Non-Relational Databases
  • Understand the Way We Use Tables, Primary Keys, and Normalization
Lesson 2: Understanding Different Data Systems
  • Describe Types of Data Processing and Storage Systems
  • Explain How Data Changes
Lesson 3: Understanding Types and Characteristics of Data
  • Understand Types of Data
  • Break Down the Field Data Types
Lesson 4: Comparing and Contrasting Different Data Structures, Formats, and Markup Languages
  • Differentiate between Structured Data and Unstructured Data
  • Recognize Different File Formats
  • Understand the Different Code Languages Used for Data
Lesson 5: Explaining Data Integration and Collection Methods
  • 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
Lesson 6: Identifying Common Reasons for Cleansing and Profiling Data
  • Learn to Profile Data
  • Address Redundant, Duplicated, and Unnecessary Data
  • Work with Missing Value
  • Address Invalid Data
  • Convert Data to Meet Specifications
Lesson 7: Executing Different Data Manipulation Techniques
  • Manipulate Field Data and Create Variables
  • Transpose and Append Data
  • Query Data
Lesson 8: Explaining Common Techniques for Data Manipulation and Optimization
  • Use Functions to Manipulate Data
  • Use Common Techniques for Query Optimization
Lesson 9: Applying Descriptive Statistical Methods
  • Use Measures of Central Tendency
  • Use Measures of Dispersion
  • Use Frequency and Percentages
Lesson 10: Describing Key Analysis Techniques
  • Get Started with Analysis
  • Recognize Types of Analysis
Lesson 11: Understanding the Use of Different Statistical Methods
  • Understand the Importance of Statistical Tests
  • Break Down the Hypothesis Test
  • Understand Tests and Methods to Determine Relationships Between Variables
Lesson 12: Using the Appropriate Type of Visualization
  • Use Basic Visuals
  • Build Advanced Visuals
  • Build Maps with Geographical Data
  • Use Visuals to Tell a Story
Lesson 13: Expressing Business Requirements in a Report Format
  • 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
Lesson 14: Designing Components for Reports and Dashboards
  • Design Elements for Reports and Dashboards
  • Utilize Standard Elements
  • Creating a Narrative and Other Written Elements
  • Understand Deployment Considerations
Lesson 15: Understand Deployment Considerations
  • Understand How Updates and Timing Affect Reporting
  • Differentiate Between Types of Reports
Lesson 16: Summarizing the Importance of Data Governance
  • Define Data Governance
  • Understand Access Requirements and Policies
  • Understand Security Requirements
  • Understand Entity Relationship Requirements
Lesson 17: Applying Quality Control to Data
  • Describe Characteristics, Rules, and Metrics of Data Quality
  • Identify Reasons to Quality Check Data and Methods of Data Validation
Lesson 18: Explaining Master Data Management Concepts
  • Explain the Basics of Master Data Management
  • Describe Master Data Management Processes

Available Formats

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