CompTIA Data+ Training

Course Duration

5 Days

Audience

Employees of federal, state and local governments; and businesses working with the government.

Prerequisites

CompTIA recommends 18–24 months of experience in a report or business analyst role, exposure to databases and analytical tools, a basic understanding of statistics, and data visualization experience.

Course Description

The CompTIA Data+ certification is an early-career data analytics certification that validates the skills required to collect, analyze, interpret, and present data with clarity and confidence. This 5-day course prepares you for the CompTIA Data+ exam (DA0-002). You will gain hands-on data analytics experience across the entire data lifecycle — from data mining and transformation to data visualization and reporting — while applying governance and quality standards throughout.

Learning Objectives

• Mine data from multiple sources, clean and manipulate it for analysis
• Apply basic statistical methods to interpret data effectively
• Maintain data governance and quality standards throughout the data lifecycle
• Build compelling visualizations and dashboards to support business decisions
• Design and deploy reports tailored to specific audiences and business needs
• Understand master data management and data quality control processes
• Prepare for and pass the CompTIA Data+ (DA0-002) certification exam

Course Outline

1 – Identifying Basic Concepts of Data Schemas

• Relational vs. non-relational databases
• Tables, primary keys, normalization

2 – Understanding Different Data Systems

• Types of data processing and storage
• How data changes over time

3 – Understanding Types and Characteristics of Data

• Structured vs. unstructured data
• File types and field data

4 – Comparing Data Structures, Formats, and Languages

• CSV, JSON, XML
• Common code languages used for data

5 – Data Integration and Collection

• ETL processes
• API and web scraping
• Public and survey data collection

6 – Data Cleansing and Profiling

• Handling missing or invalid data
• Converting and validating data

7 – Data Manipulation Techniques

• Querying, appending, and transforming data
• Creating calculated fields and variables

8 – Data Optimization

• Query optimization
• Efficient function use

9 – Descriptive Statistics

• Central tendency and dispersion
• Frequency and percentages

10 – Key Analytical Techniques

• Introduction to common analysis types

11 – Statistical Methods

• Hypothesis testing
• Relationships between variables

12 – Data Visualization

• Basic and advanced visuals
• Geographical data maps
• Telling stories with visuals

13 – Business Reporting Requirements

• Identifying audience needs
• Data source considerations
• Sorting and filtering

14 – Designing Reports and Dashboards

• Narrative and design elements
• Deployment considerations

15 – Deployment Considerations

• Timing, updating, and types of reports

16 – Data Governance

• Access policies
• Security and entity relationship rules

17 – Data Quality Control

• Data validation and quality metrics

18 – Master Data Management

• MDM basics and processes

Frequently Asked Questions