CompTIA Data+

 5 Days
Delivery Methods
 VILT    Private Group
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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.


Upcoming Class Dates and Times

Jan 09  - Jan 13, 2023
8:00AM - 4:00PM Central
5 Days
Virtual Instructor Led
Apr 17  - Apr 21, 2023
8:00AM - 4:00PM Central
5 Days
Virtual Instructor Led
Jul 17  - Jul 21, 2023
8:00AM - 4:00PM Central
5 Days
Virtual Instructor Led

Course Objectives

    On course completion, participants will be able to:
  • 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


1 - Identifying Basic Concepts of Data Schemas
  • Identify Relational and Non-Relational Databases
  • Understand the Way We Use Tables, Primary Keys, and Normalization
2 - Understanding Different Data Systems
  • Describe Types of Data Processing and Storage Systems
  • Explain How Data Changes
3 - Understanding Types and Characteristics of Data
  • Understand Types of Data
  • Break Down the Field Data Types
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
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
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
7 - Executing Different Data Manipulation Techniques
  • Manipulate Field Data and Create Variables
  • Transpose and Append Data
  • Query Data
8 - Explaining Common Techniques for Data Manipulation and Optimization
  • Use Functions to Manipulate Data
  • Use Common Techniques for Query Optimization
9 - Applying Descriptive Statistical Methods
  • Use Measures of Central Tendency
  • Use Measures of Dispersion
  • Use Frequency and Percentages
10 - Describing Key Analysis Techniques
  • Get Started with Analysis
  • Recognize Types of Analysis
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
12 - Using the Appropriate Type of Visualization
  • Use Basic Visuals
  • Build Advanced Visuals
  • Build Maps with Geographical Data
  • Use Visuals to Tell a Story
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
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
15 - Understand Deployment Considerations
  • Understand How Updates and Timing Affect Reporting
  • Differentiate Between Types of Reports
16 - Summarizing the Importance of Data Governance
  • Define Data Governance
  • Understand Access Requirements and Policies
  • Understand Security Requirements
  • Understand Entity Relationship Requirements
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
18 - Explaining Master Data Management Concepts
  • Explain the Basics of Master Data Management
  • Describe Master Data Management Processes

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