MPD 204 - Data+ Exam Preparation

Course Meeting Dates and Times:
  • Days/Times: Tuesdays, 6-9 p.m.
  • Dates: January 10-April 25
Location:

Catholic University of America-Alexandria
2050 Ballenger Ave. #200
Alexandria VA 22314
OR
Participate live online through Zoom from anywhere

Price per student: $1500 (includes textbooks, practice tests, and exam voucher)
Instructor: TBA
Textbook: The Official CompTIA Data+ Student Guide (Exam DA0-001), by Robin E. Hunt, 2022


Exam details:

Exam Codes

DA0-001

Launch Date

February 28, 2022 

Exam Description

The CompTIA Data+ exam will certify the successful candidate has the knowledge and skills required to transform business requirements in support of data-driven decisions through mining and manipulating data, applying basic statistical methods, and analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle.

Number of Questions

90 questions

Type of Questions

Multiple choice and performance-based

Length of Test

90 Minutes

Passing Score

675 (on scale of 100–900)

Recommended Experience

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

Languages 

English 

Retirement

Usually three years after launch  

Course Learning Objectives

Data Concepts and Environments:
Identify basic concepts of data schemas and dimensions.
Compare and contrast different data types.
Compare and contrast common data structures and file formats.

Data Mining:
Explain data acquisition concepts.
Identify common reasons for cleansing and profiling datasets.
Given a scenario, execute data manipulation techniques.
Explain common techniques for data manipulation and query optimization.

Data Analysis:
Given a scenario, apply the appropriate descriptive statistical methods.
Explain the purpose of inferential statistical methods. 
Summarize types of analysis and key analysis techniques.
Identify common data analytics tools.

Visualization:
Given a scenario, translate business requirements to form a report.
Given a scenario, use appropriate design  components for reports and dashboards.
Given a scenario, use appropriate methods for dashboard development.
Given a scenario, apply the appropriate type of visualization.
Compare and contrast types of reports.

Data Governance, Quality, and Controls:
Summarize important data governance concepts.
Given a scenario, apply data quality control concepts.
Explain master data management (MDM) concepts.