CompTIA Data+

CompTIA's Data+ Certification is an early-career data analytics certification for professionals tasked with developing and promoting data-driven business decision-making.

The exam will certify the successful candidate has the knowledge and skills required to transform business requirements in support of data-driven decisions by:

• Mining data

• Manipulating data

• Applying basic statistical methods

• Analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle

This course can benefit you in two ways. If you intend to pass the CompTIA Data+(Exam DA0-001) certification examination, this course can be a significant part of your preparation. But certification is not the only key to professional success in the field of data analysis. Today's job market demands individuals with demonstrable skills, and the information and activities in this course can help you build your data skill set so that you can confidently perform your duties in any entry-level data analysis role.

On course completion, you will be able to do the following:

• Identify basic concepts of data schemas

• Understand different data systems

• Understand types and characteristics of data

• Compare and contrast different data structures, formats, and markup languages

• Explain data integration and collection methods

• Identify common reasons for cleansing and profiling data

• Execute different data manipulation techniques

• Explain common techniques for data manipulation and optimization

• Apply descriptive statistical methods

• Describe key analysis techniques

• Understand the use of different statistical methods

• Use the appropriate type of visualization

• Express business requirements in a report format

• Design components for reports and dashboards

• Distinguish different report types

• Summarize the importance of data governance

• Apply quality control to data

• Explain master data management concepts

• Identify common data analytics tools

• To ensure your success in this course, you should have 18–24 months of hands-on experience working in a business intelligence, report/data analyst job role.

• You should have a working knowledge of Microsoft Excel or a spreadsheet program.

• You should understand how to build basic math calculations, like add, subtract, divide, and multiply (basic arithmetic).

• You should know how to build basic functions like Sums, Average, and Count.

• You should understand the basics of sorting and filtering data sets in Excel or a similar spreadsheet program.

• You should have a working knowledge of how to build very basic pivot tables.

• You should have some understanding of databases and all knowledge toward understanding how databases designed will be helpful.

• You should have a basic understanding of how to build simple charts in using data.

Lesson 1: Identifying Basic Concepts of Data Schemas

Topic 1A: Identify Relational and Non-Relational Databases

Topic 1B: Understand the Way We Use Tables, Primary Keys, and Normalization

Lesson 2: Understanding Different Data Systems

Topic 2A: Describe Types of Data Processing and Storage Systems

Topic 2B: Explain How Data Changes

Lesson 3: Understanding Types and Characteristics of Data

Topic 3A: Understand Types of Data

Topic 3B: Break Down the Field Data Types

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

Topic 4A: Differentiate Between Structured Data and Unstructured Data

Topic 4B: Recognize Different File Formats

Topic 4C: Understand the Different Code Languages Used for Data

Lesson 5: Explaining Data Integration and Collection Methods

Topic 5A: Understand the Processes of Extracting, Transforming and Loading Data

Topic 5B: Explain API/Web Scraping and Other Collection Methods

Topic 5C: Collect and Use Public and Publicly Available Data

Topic 5D: Use and Collect Survey Data

Lesson 6: Identifying Common Reasons for Cleansing and Profiling Data

Topic 6A: Learn to Profile Data

Topic 6B: Address Redundant, Duplicated, and Unnecessary Data

Topic 6C: Work with Missing Values

Topic 6D: Address Invalid Data

Topic 6E: Convert Data to Meet Specifications

Lesson 7: Executing Different Data Manipulation Techniques

Topic 7A: Manipulate Field Data and Create Variables

Topic 7B: Transpose and Append Data

Topic 7C: Query Data

Lesson 8: Explaining Common Techniques for Data Manipulation

and Optimization

Topic 8A: Use Functions to Manipulate Data

Topic 8B: Use Common Techniques for Query Optimization

Lesson 9: Applying Descriptive Statistical Methods

Topic 9A: Use Measures of Central Tendency

Topic 9B: Use Measures of Dispersion

Topic 9C: Use Frequency and Percentages

Lesson 10: Describing Key Analysis Techniques

Topic 10A: Get Started with Analysis

Topic 10B: Recognize Types of Analysis

Lesson 11: Understanding the Use of Different Statistical Methods

Topic 11A: Understand the Importance of Statistical Tests

Topic 11B: Break Down the Hypothesis Test

Topic 11C: Understand Tests and Methods to Determine Relationships Between Variables

Lesson 12: Using the Appropriate Type of Visualization

Topic 12A: Use Basic Visuals

Topic 12B: Build Advanced Visuals

Topic 12C: Build Maps with Geographical Data

Topic 12D: Use Visuals to Tell a Story

Lesson 13: Expressing Business Requirements in a Report Format

Topic 13A: Consider Audience Needs When Developing a Report

Topic 13B: Describe Data Source Considerations for Reporting

Topic 13C: Describe Considerations for Delivering Reports and Dashboards

Topic 13D: Develop Reports or Dashboards

Topic 13E: Understand Ways to Sort and Filter Data

Lesson 14: Designing Components for Reports and Dashboards

Topic 14A: Choose Design Elements for Reports/Dashboards

Topic 14B: Utilize Standard Elements for Reports/Dashboards

Topic 14C: Create a Narrative and Other Written Elements

Topic 14D: Understand Deployment Considerations

Lesson 15: Distinguishing Different Report Types

Topic 15A: Understand How Updates and Timing Affect Reporting

Topic 15B: Differentiate Between Types of Reports

Lesson 16: Summarizing the Importance of Data Governance

Topic 16A: Define Data Governance

Topic 16B: Understand Access Requirements and Policies

Topic 16C: Understand Security Requirements

Topic 16D: Understand Entity Relationship Requirements

Lesson 17: Applying Quality Control to Data

Topic 17A: Describe Characteristics, Rules, and Metrics of Data Quality

Topic 17B: Identify Reasons to Quality Check Data and Methods of Data Validation

Lesson 18: Explaining Master Data Management Concepts

Topic 18A: Explain the Basics of Master Data Management

Topic 18B: Describe Master Data Management Processes

Appendix A: Identifying Common Data Analytics Tools

Appendix B: Mapping Course Content to CompTIA Data+ Certification (DA0-001)