Course Overview:

CompTIA Data+ is an early-career data analytics certification for professionals tasked with developing and promoting data-driven business decision-making. CompTIA Data+ gives you the confidence to bring data analysis to life.

As the importance for data analytics grows, more job roles are required to set context and better communicate vital business intelligence. Collecting, analyzing, and reporting on data can drive priorities and lead business decision-making.

Course Objectives:

• 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

Who Should Attend?

Data+ is an ideal certification for not only data-specific careers, but other career paths can benefit from analytics processes and data analytics knowledge. Jobs like marketing specialists, financial analysts, human resource analysts or clinical health care analysts can optimize performance and make well-informed decisions when they use and evaluate data correctly.

Course Prerequisites

There are no prerequisites for this course.

Course Content:

Module 1 – Identifying Basic Concepts of Data Schemas
Identify Relational and Non-Relational Databases
Understand the Way We Use Tables, Primary Keys, and Normalization

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
Recognize 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 Value
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 Optimization
Use Functions to Manipulate Data
Use Common Techniques for Query Optimization

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
Recognize 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 Visualization
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
Utilize Standard Elements
Creating a Narrative and Other Written Elements
Understand Deployment Considerations

Module 15 – Understand Deployment Considerations
Understand How Updates and Timing Affect Reporting
Differentiate Between Types of Reports

Module 16 – Summarizing 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