CompTIA Data+

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.

Days : 5
Price :





  • 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.

Course Objectives

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.

Outline: CompTIA Data+ (DATA+)

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