As the importance of data analytics grows, more job roles are required to set a context and better communicate vital business intelligence. Collecting, analysing, and reporting data can drive priorities and lead business decision-making. CompTIA Data+ certification validates professionals have the skills required to facilitate data-driven business decisions, including:

• Mining data
• Manipulating data
• Visualising and reporting data
• Applying basic statistical methods
• Analysing complex datasets while adhering to governance and quality standards throughout the entire data life cycle

Examination Information

• CompTIA DA0-001
• Testing through Pearson VUE

In this CompTIA Data+ course, you will learn:

• Instruction from CompTIA approved Data+ Certification preparation course.
• Receive a CompTIA Data+ Exam Voucher included upon completion of the course.
• Identify Data Concepts and Environments important in analytics.
• Execute techniques in Data Mining, Data Mining, and Visualisation.
• Summarise the importance of Data Governance, Quality, and Controls.
• Continue learning and face new challenges with after-course one-on-one instructor coaching.

Module 1: Identifying Basic Concepts of Data Schemas

• Identify Relational and Non-Relational Databases
• Understand the Way We Use Tables, Primary Keys, and Normalisation

Module 2: Understanding Different Data Systems

• Describe Types of Data Processing and Storage Systems
• Explain How Data Changes

Module 3: Understanding Types and Characteristics of Data

• Understand Types of Data
• Break Down the Field Data Types

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

• Differentiate between Structured Data and Unstructured Data
• Recognise Different File Formats
• Understand the Different Code Languages Used for Data

Module 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

Module 6: Identifying Common Reasons for Cleansing and Profiling Data

• Learn to Profile Data
• Address Redundant, Duplicated, and Unnecessary Data
• Work with Missing Values
• Address Invalid Data
• Convert Data to Meet Specifications

Module 7: Executing Different Data Manipulation Techniques

• Manipulate Field Data and Create Variables
• Transpose and Append Data
• Query Data

Module 8: Explaining Common Techniques for Data Manipulation and Optimisation

• Use Functions to Manipulate Data
• Use Common Techniques for Query Optimisation

Module 9: Applying Descriptive Statistical Methods

• Use Measures of Central Tendency
• Use Measures of Dispersion
• Use Frequency and Percentages

Module 10: Describing Key Analysis Techniques

• Get Started with Analysis
• Recognise Types of Analysis

Module 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

Module 12: Using the Appropriate Type of Visualisation

• Use Basic Visuals
• Build Advanced Visuals
• Build Maps with Geographical Data
• Use Visuals to Tell a Story

Module 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

Module 14: Designing Components for Reports and Dashboards

• Design Elements for Reports and Dashboards
• Utilise Standard Elements
• Creating a Narrative and Other Written Elements
• Understand Deployment Considerations

Module 15: Distinguishing Different Report Types

• Understand How Updates and Timing Affect Reporting
• Differentiate Between Types of Reports

Module 16: Summarising the Importance of Data Governance

• Define Data Governance
• Understand Access Requirements and Policies
• Understand Security Requirements
• Understand Entity Relationship Requirements

Module 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

Module 18: Explaining Master Data Management Concepts

• Explain the Basics of Master Data Management
• Describe Master Data Management Processes

• CompTIA recommends 18–24 months of experience in a report/business analyst job to succeed in this course.
• Exposure to databases and analytical tools, a basic understanding of statistics, and data visualisation experiences, such as Excel, Power BI, and Tableau.