CompTIA Data+ (DA0-002) - Pearson Cert Prep (Video Course)

Description

Almost 7 Hours of Video Instruction

Earn your CompTIA Data+ certification and build practical data skills

Overview:

CompTIA Data+ (DA0-002) is a comprehensive video training course that maps directly to the five official domains of the Data+ exam. Designed for professionals with 1.5 to 2 years of hands-on experience with databases, analytical tools, statistics, and visualization, the course builds the practical skills needed to earn the Data+ credential and apply data analytics on the job.

Hands-on demonstrations run throughout, including SQL queries, Python/pandas data wrangling, regular expression pattern matching, and BI dashboard building. Because each module (Data Concepts and Environments, Data Acquisition and Preparation, Data Analysis, Data Visualization and Reporting, and Data Governance and Privacy) also stands on its own, the course doubles as a targeted skills refresher for working professionals who dont need full certification.

Topics include

• Data concepts, structures, and environments: Relational and nonrelational databases, file types, and cloud infrastructure
• Data acquisition and preparation: Querying, ETL/ELT pipelines, SQL, and Python/pandas data wrangling
• Data analysis: Statistical methods, KPIs, and troubleshooting common data and SQL errors
• Data visualization and reporting: Choosing chart types, dashboard design, and stakeholder communication
• Data governance and compliance: GDPR, PCI DSS, privacy protection, and quality assurance

Skill Level:

Beginner to Intermediate

Learn How To:

• Query and manipulate data using SQL (joins, aggregations, subqueries, optimization) and Python/pandas
• Work with relational and nonrelational databases, data repositories, file types, and cloud infrastructure
• Apply statistical methods (descriptive, inferential, predictive, and prescriptive) to answer real business questions
• Build and validate data visualizations and dashboards using industry-standard BI tools
• Implement data governance, compliance frameworks (GDPR, PCI DSS), privacy protection, and quality assurance practices

Course requirement:

• 5 to 2 years of hands-on experience with databases, analytical tools, statistics, and/or data visualization (recommended)
• Familiarity with basic database concepts (tables, rows, columns) and at least one query language (SQL preferred)
• Basic comfort with spreadsheet tools (Excel or similar)
• No advanced programming experience requiredPython examples are explained from first principles

Who Should Take This Course:

• Primary: IT professionals and data practitioners with 1.5 to 2 years of hands-on experience with databases, analytical tools, statistics, and data visualization who are preparing to sit for the CompTIA Data+ (DA0-002) certification exam
• Secondary: Business analysts, reporting analysts, and data operations professionals looking to formalize their data skills, plus IT generalists and developers transitioning into data-focused roles and enterprise teams building modular data-skills training

About Pearson Video Training

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification. Topics include IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, Artificial Intelligence, and more. Learn more about Pearson Video training at http://www.informit.com/video.

Sample Content

Table of Contents

Module 1: What to Expect
Lesson 1: Introduction and Exam Overview
1.1 About CompTIA Data+ V2 (DA0-002)
1.2 What changed from V1 (DA0-001) to V2 (DA0-002)
1.3 Exam format and study strategy
1.4 Course roadmap
Module 2: Data Concepts and Environments
Lesson 2: Data Concepts
2.1 Relational vs. non-relational databases
2.2 File extensions and when you will see them
2.3 Structured data: tables, schemas, and dimensional modeling
2.4 Semi-structured and unstructured data
2.5 Data types deep dive
Lesson 3: Data Sources, Infrastructure, Tools, and AI Concepts
3.1 Data sources: where data comes from
3.2 Data repositories: Lakes, warehouses, and beyond
3.3 Cloud and on-premises infrastructure
3.4 Storage types and containerization
3.5 Common data analysis tools
3.6 AI concepts for data analysts
Module 3: Data Acquisition and Preparation
Lesson 4: Data Acquisition Methods
4.1 Data integration concepts
4.2 Querying fundamentals
4.3 Basic query optimization
4.4 ETL vs. ELT: When and why
4.5 Data collection: Surveying and sampling methods
Lesson 5: Data Exploration, Transformation, and Cleansing
5.1 Exploring data for inconsistencies
5.2 Completeness, redundancy, and validation
5.3 String manipulation and regular expressions
5.4 Transformation techniques
5.5 Merging, appending, parsing, and derived variables
Module 4: Data Analysis
Lesson 6: Communication Approaches and Statistical Methods
6.1 Determining the communication approach: Audience and context
6.2 Mock-ups and accessibility
6.3 KPIs and business requirements
6.4 Statistical methods: Descriptive, inferential, predictive, prescriptive
6.5 Functions and measures
Lesson 7: Troubleshooting Data Analysis Issues
7.1 Connectivity and user-reported issues
7.2 Troubleshooting basic SQL code errors
7.3 Dealing with corrupted data
7.4 Tools and methods for troubleshooting
Module 5: Data Visualization and Reporting
Lesson 8: Visual Elements and Design
8.1 Choosing the right visual type
8.2 When to use what: Matching data to visual type
8.3 Design elements: Labels, legends, branding, color
8.4 Common visualization mistakes and how to avoid them
Lesson 9: Delivery Methods and Report Validation
9.1 Executive summaries and self-service portals
9.2 Dashboards: Static, dynamic, recurring, ad hoc
9.3 Data versioning: Snapshots and real-time
9.4 Report validation issues
9.5 Validation techniques
Module 6: Data Governance and Privacy
Lesson 10: Data Management and Compliance
10.1 Data management: Integration, source of truth, versioning, metadata
10.2 Documentation practices
10.3 Data compliance frameworks
10.4 Audit, classification, and incident reporting
Lesson 11: Data Privacy, Protection, and Quality Assurance
11.1 Data privacy and protection: Access control and encryption
11.2 PII, PHI, anonymization, and masking
11.3 Quality assurance: Testing practices
11.4 Data health checks, profiling, monitoring, and ISO standards
Module 7: What's Next?
Lesson 12: Exam Prep and Next Steps
12.1 Exam day tips and final review
12.2 Summary and next steps

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