Who should take this course?
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.
Prerequisites
None
The Training Covers These Topics:
- Manage server hardware.
- Install server hardware and operating systems.
- Configure networking hardware and protocols.
- Perform basic server configuration tasks.
- Create a virtual server environment.
- Administer servers.
- Implement server storage solutions.
- Secure the server.
- Plan and test disaster recovery.
- Troubleshoot server issues.
1 – Identifying Basic Concepts of Data Schemas
- Identify Relational and Non-Relational Databases
- Understand the Way We Use Tables, Primary Keys, and Normalization
2 – Understanding Different Data Systems
- Describe Types of Data Processing and Storage Systems
- Explain How Data Changes
3 – Understanding Types and Characteristics of Data
- Understand Types of Data
- Break Down the Field Data Types
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
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
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
7 – Executing Different Data Manipulation Techniques
- Manipulate Field Data and Create Variables
- Transpose and Append Data
- Query Data
8 – Explaining Common Techniques for Data Manipulation and Optimization
- Use Functions to Manipulate Data
- Use Common Techniques for Query Optimization
9 – Applying Descriptive Statistical Methods
- Use Measures of Central Tendency
- Use Measures of Dispersion
- Use Frequency and Percentages
10 – Describing Key Analysis Techniques
- Get Started with Analysis
- Recognize Types of Analysis
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
12 – Using the Appropriate Type of Visualization
- Use Basic Visuals
- Build Advanced Visuals
- Build Maps with Geographical Data
- Use Visuals to Tell a Story
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
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
15 – Understand Deployment Considerations
- Understand How Updates and Timing Affect Reporting
- Differentiate Between Types of Reports
16 – Summarizing the Importance of Data Governance
- Define Data Governance
- Understand Access Requirements and Policies
- Understand Security Requirements
- Understand Entity Relationship Requirements
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
18 – Explaining Master Data Management Concepts
- Explain the Basics of Master Data Management
- Describe Master Data Management Processes