As the importance of data analytics grows, more job roles are required to set a context and better communicate vital business intelligence. Collecting, analyzing, 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
Visualizing and reporting data
Applying basic statistical methods
Analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle
CompTIA Data+ Training Delivery Methods
In-Person
Online
CompTIA Data+ Training Information
In this CompTIA Data+ course, you will learn how to:
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 Visualization.
Summarize the importance of Data Governance, Quality, and Controls.
Continue learning and face new challenges with after-course one-on-one instructor coaching.
CompTIA Data+ Training Prerequisites
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 visualization experiences, such as Excel, Power BI, and Tableau.
CompTIA Data+ Certification Information
CompTIA DA0-001
Testing through Pearson VUE
CompTIA Data+ Instructor-Led Course Outline
Module 1: Identifying Basic Concepts of Data Schemas
In this module, you will learn how to:
Identify Relational and Non-Relational Databases
Understand the Way We Use Tables, Primary Keys, and Normalization
Module 2: Understanding Different Data Systems
Module 3: Understanding Types and Characteristics of Data
Module 4: Comparing and Contrasting Different Data Structures, Formats, and Markup Languages
Module 5: Explaining Data Integration and Collection Methods
Module 6: Identifying Common Reasons for Cleansing and Profiling Data
Module 7: Executing Different Data Manipulation Techniques
Module 8: Explaining Common Techniques for Data Manipulation and Optimization
Module 9: Applying Descriptive Statistical Methods
Module 10: Describing Key Analysis Techniques
Module 11: Understanding the Use of Different Statistical Methods
Module 12: Using the Appropriate Type of Visualization
Module 13: Expressing Business Requirements in a Report Format
Module 14: Designing Components for Reports and Dashboards
Module 15: Distinguishing Different Report Types
Module 16: Summarizing the Importance of Data Governance
Module 17: Applying Quality Control to Data
Module 18: Explaining Master Data Management Concepts
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