Training plan
Module 1: Identifying Basic Concepts of Data Schemas
Introduction to the fundamental concepts of data schemas and their role in structuring information.
Module 2: Understanding Different Data Systems
Exploration of various data systems, such as relational and non-relational databases, and their uses.
Module 3: Understanding Types and Characteristics of Data
Study of different data types (structured, semi-structured, unstructured) and their specific characteristics.
Module 4: Comparing and Contrasting Different Data Structures, Formats, and Markup Languages
Analysis of the differences between various data structures, file formats, and markup languages like XML and JSON.
Module 5: Explaining Data Integration and Collection Methods
Overview of techniques and tools used to integrate and collect data from various sources.
Module 6: Identifying Common Reasons for Cleansing and Profiling Data
Discussion on the importance of data cleaning and profiling to ensure their quality and utility.
Module 7: Executing Different Data Manipulation Techniques
Demonstration of common techniques for manipulating data, such as transformation, aggregation, and filtering.
Module 8: Explaining Common Techniques for Data Manipulation and Optimization
Examination of methods used to optimize and manipulate data to improve their performance and utility.
Module 9: Applying Descriptive Statistical Methods
Use of descriptive statistical techniques to analyze and summarize data.
Module 10: Describing Key Analysis Techniques
Overview of data analysis techniques, such as exploratory and predictive analysis.
Module 11: Understanding the Use of Different Statistical Methods
Exploration of various statistical methods and their application in data analysis.
Module 12: Using the Appropriate Type of Visualization
Selection and use of the most appropriate visualization types to represent data clearly and effectively.
Module 13: Expressing Business Requirements in a Report Format
Translation of business requirements into clear and accurate reports.
Module 14: Designing Components for Reports and Dashboards
Creation and organization of report components and dashboards for optimal data visualization.
Module 15: Distinguishing Different Report Types
Identification and differentiation of various types of reports used in business.
Module 16: Summarizing the Importance of Data Governance
Discussion on the need for data governance to ensure data quality, security, and compliance.
Module 17: Applying Quality Control to Data
Implementation of quality control techniques to verify and maintain data accuracy and reliability.
Module 18: Explaining Master Data Management Concepts
Introduction to the principles of master data management and their importance in ensuring information consistency across the organization.
Recommended Prerequisite Knowledge
- Basic computer skills: Proficiency in using a computer and navigating different operating systems.
- Fundamental understanding of data concepts: Familiarity with basic data concepts such as databases, data types, and data formats.
- Basic math and statistics skills: Knowledge of fundamental mathematics and statistics, essential for understanding data analysis techniques.
- Experience with office productivity tools: Experience using tools like spreadsheets (e.g., Microsoft Excel), often used for data manipulation and analysis.
Credentials and certification
Exam features
- Code: DA0-001
- Title: CompTIA Data+
- Duration: 90 minutes
- Number of Questions: 90
- Questions Format: Multiple-choice, multiple-answer
- Passing Score: 675 out of 900
- Cost: $0 (included in your training)
Exam topics
The CompTIA Data+ exam will certify that the successful candidate possesses the knowledge and skills required to transform business requirements into data-driven decisions by exploring and manipulating data, applying basic statistical methods, and analyzing complex data sets while adhering to governance and quality standards throughout the data lifecycle.