CompTIA Data+® Certification Training

Unlock the power of data-driven decision-making with the CompTIA Data+ Certification Training! As the importance of data analytics continues to grow, more job roles are being created to set a context and better communicate vital business intelligence. This comprehensive course will teach you the skills required to facilitate data-driven business decisions, including mining, manipulating, visualizing, and reporting data. 

Learn to apply basic statistical methods and analyze complex datasets while adhering to governance and quality standards throughout the entire data life cycle. The course also covers data integration and collection methods, data governance tools, and how to express business requirements in a report format. 

Upon completing the course, you will receive a CompTIA Data+ Exam Voucher and the opportunity to work with an instructor for after-course one-on-one coaching. If you're a data analyst, reporting analyst, business intelligence analyst, clinical analyst, marketing analyst, operations analyst, or simply someone interested in data analytics, this course is for you!

CompTIA Data+® Certification Training Benefits

• In this course, you will:

  • Participate in Instruction from CompTIA-certifiedData+ 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.

• Training Prerequisites

  • CompTIA recommends 18–24 months (about 2 years) 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 (Business Intelligence), and Tableau.

• 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

In this module, you will learn how to:

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

Module 3: Understanding Types and Characteristics of Data

In this module, you will learn how to:

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

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

In this module, you will learn how to:

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

Module 5: Explaining Data Integration and Collection Methods

In this module, you will learn how to:

• 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

In this module, you will learn how to:

• 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

In this module, you will learn how to:

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

Module 8: Explaining Common Techniques for Data Manipulation and Optimization

In this module, you will learn how to:

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

Module 9: Applying Descriptive Statistical Methods

In this module, you will learn how to:

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

Module 10: Describing Key Analysis Techniques

In this module, you will learn how to:

• Get Started with Analysis
• Recognize Types of Analysis

Module 11: Understanding the Use of Different Statistical Methods

In this module, you will learn how to:

• 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 Visualization

In this module, you will learn how to:

• 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

In this module, you will learn how to:

• 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

In this module, you will learn how to:

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

Module 15: Distinguishing Different Report Types

In this module, you will learn how to:

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

Module 16: Summarizing the Importance of Data Governance

In this module, you will learn how to:

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

Module 17: Applying Quality Control to Data

In this module, you will learn how to:

• 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

In this module, you will learn how to:

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