CompTIA Data+ Certification Training Course

Lesson 1: Identifying Basic Concepts of Data Schemas

• Understand the fundamental concepts of data schemas.
• Learn how data schemas are structured and used in business environments.
• Explore the role of schemas in data storage and organization.

Lesson 2: Understanding Different Data Systems

• Learn about various data systems and their purposes.
• Compare and contrast relational and non-relational data systems.
• Understand the advantages and challenges of each system type.

Lesson 3: Understanding Types and Characteristics of Data

• Explore different data types (e.g., structured, semi-structured, unstructured).
• Understand the characteristics of data and how they impact analysis.
• Examine data quality and its importance in decision-making.

Lesson 4: Comparing and Contrasting Different Data Structures, Formats, and Markup Languages

• Learn about common data structures and formats used in analytics.
• Understand how markup languages influence data representation.
• Explore how these structures impact data integration and analysis.

Lesson 5: Explaining Data Integration and Collection Methods

• Learn the methods used for collecting and integrating data from various sources.
• Understand best practices for ensuring accurate data collection.
• Explore tools and techniques used in data integration.

Lesson 6: Identifying Common Reasons for Cleansing and Profiling Data

• Understand the importance of data cleansing and profiling.
• Learn techniques for identifying data quality issues.
• Examine strategies to improve data integrity.

Lesson 7: Executing Different Data Manipulation Techniques

• Explore data manipulation techniques such as filtering, sorting, and transforming data.
• Learn how to apply these techniques to clean and optimize data.

Lesson 8: Explaining Common Techniques for Data Manipulation and Optimization

• Understand optimization strategies for handling large datasets.
• Learn about the role of indexing and query optimization in data systems.

Lesson 9: Applying Descriptive Statistical Methods

• Learn how to apply descriptive statistics such as mean, median, and mode.
• Understand the importance of these methods in data analysis.

Lesson 10: Describing Key Analysis Techniques

• Examine key analysis techniques such as regression and correlation.
• Understand how these techniques help in drawing business conclusions.

Lesson 11: Understanding the Use of Different Statistical Methods

• Learn about various statistical methods used in data analysis.
• Explore the application of these methods in solving real-world business problems.

Lesson 12: Using the Appropriate Type of Visualization

• Learn how to select and use the correct type of visualization for different data sets.
• Understand the principles of data visualization design.

Lesson 13: Expressing Business Requirements in a Report Format

• Learn how to translate business requirements into structured data reports.
• Understand how to present data in an easily understandable format for stakeholders.

Lesson 14: Designing Components for Reports and Dashboards

• Learn how to design effective components for reports and dashboards.
• Understand best practices in report design for data-driven decisions.

Lesson 15: Distinguishing Different Report Types

• Understand the differences between operational, analytical, and strategic reports.
• Learn how to choose the right report type based on business needs.

Lesson 16: Summarizing the Importance of Data Governance

• Understand the concept of data governance and its impact on data management.
• Learn about policies, procedures, and standards for managing data governance.

Lesson 17: Applying Quality Control to Data

• Learn quality control techniques and their application in data management.
• Understand how to maintain data quality throughout the lifecycle.

Lesson 18: Explaining Master Data Management Concepts

• Learn about master data management and its importance in managing critical business data.
• Understand how master data management ensures consistency and accuracy across systems.