CompTIA Data+ V2 (New Version)
This course prepares participants for the CompTIA Data+ V2 certification, covering data concepts and environments, data acquisition and preparation, data analysis, visualization and reporting, and data governance. Through lectures, guided examples, hands-on exercises, and exam-style practice, learners work with business data workflows from source selection to reporting.
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Description
This course prepares participants for the CompTIA Data+ V2 certification, covering data concepts and environments, data acquisition and preparation, data analysis, visualization and reporting, and data governance.
Through lectures, guided examples, hands-on exercises, and exam-style practice, learners work with business data workflows from source selection to reporting.
Topics include data quality checks, basic statistics, dashboards, report validation, governance controls, and data life cycle concepts.
After completion, participants should be able to prepare datasets, interpret results, present findings, and support data-informed decisions.
What You Will Learn
Module 1: Data Concepts and Environments
Core data types, including structured, semi-structured, and unstructured data.
Relational and non-relational databases, data warehouses, data lakes, data marts, and common file formats such as CSV, XLSX, and JSON.
Basic cloud, hybrid, storage, and container concepts used in modern data work.
Module 2: Data Acquisition and Preparation
Collecting data from databases, APIs, files, websites, logs, and surveys.
SQL querying, including filtering, grouping, joins, unions, nested queries, and aggregate functions.
ETL and ELT workflows, data profiling, missing values, duplicates, outliers, validation, and cleansing.
Data transformation methods such as parsing, type conversion, RegEx, binning, scaling, merging, and derived fields.
Module 3: Data Analysis
Descriptive, predictive, prescriptive, and inferential analysis concepts.
Statistical measures such as mean, median, mode, standard deviation, variance, and percent change.
Use of logical, date, string, and mathematical functions to answer business questions.
Module 4: Visualization and Reporting
Creating charts, maps, pivot tables, dashboards, and executive summaries.
Using BI tools, notebooks, IDEs, and libraries such as pandas or tidyverse where applicable.
Report validation, refresh issues, filter problems, versioning, accessibility, and audience-specific communication.
Module 5: Data Governance
Data dictionaries, lineage, metadata, source of truth, documentation, and quality checks.
Privacy and protection topics, including RBAC, encryption, PII, PHI, anonymization, masking, PCI DSS, GDPR, NIST awareness, and incident reporting.
Training may include lectures, guided demonstrations, hands-on labs, SQL practice, case studies, and exam-style exercises. After completion, participants should be able to prepare, analyze, report, and govern data in line with the CompTIA Data+ V2, DA0-002 objectives.
Certification & Exam
CompTIA Data+ V2 prepares participants for the CompTIA Data+ certification exam, DA0-002. The certification process is separate from course completion: participants complete the training, review the official exam objectives, then register for and pass the CompTIA exam through the approved testing process.
The exam assesses practical data analytics skills, including data concepts and environments, data acquisition and preparation, data analysis, visualization and reporting, and data governance.
CompTIA recommends 18 to 24 months of experience in a data analyst or similar role, with exposure to databases, analytical tools, basic statistics, and data visualization. For current exam details, refer to the official CompTIA Data+ V2 certification page.
What You Will Achieve
After completing the course, participants will be able to:
Explain core data concepts, including relational and non-relational databases, structured, semi-structured, and unstructured data, common file formats such as CSV, XLSX, JSON, TXT, and JPG, and data types such as string, numeric, Boolean, datetime, BLOB, CLOB, GUID, and UUID.
Identify common data sources, repositories, and infrastructure models, including databases, APIs, files, logs, data lakes, data lakehouses, data marts, data warehouses, AWS, Azure, Google cloud services, public, private, and hybrid environments.
Use data acquisition methods such as SQL joins, filters, unions, grouping, aggregate functions, nested queries, ETL, ELT, surveys, and sampling to collect and prepare data for analysis.
Perform data exploration, transformation, and cleansing tasks by addressing missing values, duplicates, outliers, validation issues, string manipulation, RegEx, parsing, merging, appending, scaling, standardization, imputation, and calculated fields.
Apply basic statistical methods and functions, including descriptive, predictive, prescriptive, and inferential analysis, measures of central tendency, measures of dispersion, logical functions, date functions, and string functions.
Create appropriate visualizations and reports using charts, maps, pivot tables, dashboards, executive summaries, self-service portals, snapshots, and real-time reporting methods for technical and non-technical audiences.
Troubleshoot common data and reporting issues, including slow refresh rates, excessive load time, large data size, broken filters, stale data, corrupt data, connectivity issues, and basic SQL errors using logging, source validation, code review, peer review, and monitoring alerts.
Apply data governance, privacy, protection, and quality practices, including data dictionaries, data lineage, metadata, source of truth, retention, GDPR, PCI DSS, role-based access control, encryption at rest and in transit, PII, PHI, anonymization, masking, UAT, source control, unit testing, data profiling, and data quality monitoring.
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