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Data Plus

CompTIA Data+ (DA0-002) teaches data professionals how to govern, mine, analyze, visualize, and report data, while integrating cloud platforms and AI/ML concepts, essential for effective data-driven decision making.

Who Should Take This

The course is aimed at associate‑level data analysts, engineers, or managers with 18–24 months of hands‑on experience who need to validate their skills for the CompTIA Data+ certification. They seek to master data governance, mining, analysis, visualization, and cloud‑AI integration to support organizational data initiatives and advance their career toward senior data roles.

What's Included in AccelaStudy® AI

Adaptive Knowledge Graph

Practice Questions

Lesson Modules

Console Simulator Labs

Exam Tips & Strategy

20 Activity Formats

Course Outline

65 learning goals

1 Domain 1: Data Governance, Quality, and Controls

4 topics

Data Governance Frameworks and Policies

Identify the key components of a data governance framework including policies, standards, roles, responsibilities, and decision rights within an organization.
Describe the role of data stewardship including data owners, data custodians, and data stewards and their responsibilities in maintaining data integrity.
Apply data governance policies to classify data assets according to sensitivity levels such as public, internal, confidential, and restricted categories.
Explain data retention policies including lifecycle stages, archival strategies, and regulatory requirements governing how long data must be kept.
Analyze the impact of privacy regulations such as GDPR, CCPA, and HIPAA on data governance strategies and recommend appropriate compliance controls.

Metadata Management and Data Cataloging

Define metadata types including technical, business, and operational metadata and describe their role in data management and discovery.
Explain data catalog functionality including search, lineage tracking, and business glossary features that support data discovery across an organization.
Apply master data management principles to identify and resolve duplicate, inconsistent, or conflicting records across multiple source systems.

Data Quality Dimensions and Controls

Identify the six core data quality dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness and provide examples of each.
Apply data quality controls to detect and remediate common data issues such as missing values, duplicate records, format inconsistencies, and outliers.
Implement data validation rules including referential integrity checks, domain constraints, cross-field validations, and automated quality scoring.
Evaluate data quality assessment results to prioritize remediation efforts and recommend process improvements for ongoing data quality management.
Assess the business impact of poor data quality on decision-making, reporting accuracy, and regulatory compliance in organizational contexts.

Data Ethics and Responsible Data Use

Recognize ethical considerations in data collection, storage, and usage including informed consent, data minimization, and purpose limitation principles.
Explain data anonymization and pseudonymization techniques and classify scenarios where each approach is appropriate for privacy protection.
Evaluate bias risks in data collection and analysis processes and assess the impact of biased data on organizational decision-making outcomes.

2 Domain 2: Data Mining

5 topics

Data Collection and Sources

Identify common data collection methods including surveys, web scraping, API integrations, sensor data, transactional systems, and log files.
Classify data types as structured, semi-structured, or unstructured and describe storage considerations and processing approaches for each category.
Compare primary and secondary data sources and evaluate the tradeoffs of cost, timeliness, relevance, and quality for each in a given analytical scenario.

ETL Processes and Data Integration

Describe the stages of the ETL process including extraction from source systems, transformation logic, and loading into target data stores.
Apply data transformation techniques including data type conversions, field mapping, aggregation, normalization, and denormalization to prepare data for analysis.
Differentiate between ETL and ELT approaches and evaluate which is more appropriate for batch processing, real-time streaming, and cloud-native data architectures.

Data Profiling and Cleansing

Describe data profiling activities including column analysis, frequency distributions, pattern detection, and null value assessment for understanding dataset characteristics.
Apply data cleansing techniques to handle missing values through imputation, deletion, or flagging strategies based on the nature and extent of data gaps.
Implement deduplication strategies to identify and merge duplicate records using exact matching, fuzzy matching, and probabilistic record linkage techniques.
Evaluate the effectiveness of data cleansing operations by comparing pre-cleansing and post-cleansing quality metrics across the six data quality dimensions.

SQL Fundamentals and Data Retrieval

List the core SQL statement categories including DDL, DML, and DCL and identify common commands such as SELECT, INSERT, UPDATE, DELETE, CREATE, and ALTER.
Apply SQL SELECT statements with WHERE clauses, JOIN operations, GROUP BY aggregations, HAVING filters, and ORDER BY sorting to retrieve and organize data.
Calculate aggregate values using SQL functions including COUNT, SUM, AVG, MIN, MAX, and DISTINCT to summarize data across grouped dimensions.
Analyze query performance considerations including indexing strategies, query execution plans, and the impact of JOIN types on result sets and performance.

Data Storage Architectures

Define relational database concepts including tables, rows, columns, primary keys, foreign keys, normalization forms, and entity-relationship models.
Describe NoSQL database categories including document stores, key-value stores, column-family stores, and graph databases and their typical use cases.
Explain data warehouse architecture including fact tables, dimension tables, star schemas, snowflake schemas, and the role of OLAP versus OLTP systems.
Compare data lakes, data warehouses, and data lakehouses and assess which architecture is most appropriate for given data volume, variety, and velocity requirements.

3 Domain 3: Data Analysis

4 topics

Descriptive Statistics

Define measures of central tendency including mean, median, and mode and describe when each measure is most appropriate for summarizing a dataset.
Describe measures of dispersion including range, variance, standard deviation, and interquartile range and explain their role in understanding data spread.
Calculate descriptive statistics for a given dataset and explain the significance of skewness, kurtosis, and distribution shape on data interpretation.
Evaluate the appropriateness of descriptive statistics choices for datasets with outliers, skewed distributions, and non-normal data patterns.

Inferential Statistics and Hypothesis Testing

State the concepts of population versus sample, sampling methods, confidence intervals, and margin of error in the context of statistical inference.
Describe the hypothesis testing process including null and alternative hypotheses, p-values, significance levels, and Type I and Type II errors.
Apply hypothesis testing procedures to determine whether observed differences in data are statistically significant for a given business scenario.
Differentiate between parametric and nonparametric tests and assess which statistical test is appropriate for a given data type, distribution, and sample size.

Correlation and Regression Analysis

Define correlation coefficients including Pearson and Spearman and explain the difference between correlation and causation with practical examples.
Apply simple linear regression to model the relationship between an independent and dependent variable and interpret the slope, intercept, and R-squared values.
Evaluate regression model assumptions including linearity, independence, homoscedasticity, and normality of residuals to assess model validity.

Analytical Techniques and Methods

Identify common analytical techniques including trend analysis, time series analysis, cohort analysis, segmentation, and A/B testing and their business applications.
Apply time series decomposition to identify trend, seasonal, cyclical, and irregular components in temporal datasets for forecasting purposes.
Implement A/B testing methodology including control group design, sample size determination, random assignment, and result interpretation for decision-making.
Compare analytical techniques and assess which approach is most suitable for a given business question, data availability, and decision-making context.

4 Domain 4: Data Visualization and Reporting

3 topics

Chart Types and Visual Encodings

Identify standard chart types including bar, line, pie, scatter, histogram, heatmap, treemap, box plot, and bubble charts and describe when each is appropriate.
Classify visualization types by purpose including comparison, composition, distribution, and relationship charts and select the appropriate type for a given dataset.
Evaluate visualization effectiveness by assessing chart choice, color usage, scale selection, and labeling against data visualization best practices and anti-patterns.

Dashboard Design and KPIs

List key performance indicator categories including financial, operational, customer, and process KPIs and describe how they drive dashboard design decisions.
Apply dashboard design principles including layout hierarchy, filter controls, drill-down capability, and responsive design to create effective analytical dashboards.
Assess dashboard effectiveness by evaluating whether visualizations align with audience needs, support decision-making, and accurately represent underlying data.

Data Storytelling and Reporting

Describe the elements of data storytelling including context, narrative arc, supporting visualizations, and actionable recommendations for business audiences.
Explain report design considerations including audience analysis, executive summaries, detailed appendices, and the use of annotations to guide interpretation.
Critique data presentations by identifying misleading visualizations, inappropriate statistical claims, and anti-patterns such as truncated axes and cherry-picked data.

5 Domain 5: Cloud Platforms and AI/ML Concepts

2 topics

Cloud Data Services

Identify cloud data service categories including managed databases, data warehouses, data lakes, and streaming services across AWS, Azure, and GCP platforms.
Explain cloud data pipeline architectures including ingestion, processing, storage, and serving layers and how they differ from on-premises data workflows.
Compare cloud deployment models for data workloads including IaaS, PaaS, and SaaS and assess cost, scalability, and management tradeoffs for each.

AI and Machine Learning Foundations

Define fundamental machine learning concepts including supervised learning, unsupervised learning, classification, regression, and clustering at a conceptual level.
Classify business problems as classification, regression, or clustering tasks and explain which ML approach is suitable for each problem type.
Describe the machine learning lifecycle including data preparation, feature selection, model training, evaluation, and deployment at a high level.
Assess AI ethics considerations including bias, fairness, transparency, accountability, and the principles of responsible AI in organizational data practices.

Scope

Included Topics

All domains in the CompTIA Data+ (DA0-002) exam guide: Data Governance, Quality, and Controls (32%), Data Mining (23%), Data Analysis (25%), Data Visualization and Reporting (10%), and Cloud Platforms and AI/ML Concepts (10%).
Data governance frameworks, data stewardship, metadata management, data cataloging, master data management, data quality dimensions (accuracy, completeness, consistency, timeliness, validity, uniqueness), data classification, retention policies, and privacy regulations.
Data collection methods, ETL processes, data profiling, data cleansing techniques, data transformation, SQL fundamentals, NoSQL concepts, data warehousing, data lakes, and structured, semi-structured, and unstructured data handling.
Descriptive statistics (mean, median, mode, standard deviation), inferential statistics basics, hypothesis testing concepts, correlation, regression basics, time series analysis, A/B testing, cohort analysis, and segmentation techniques.
Chart types (bar, line, pie, scatter, histogram, heatmap, treemap), dashboard design principles, KPIs, storytelling with data, and visualization best practices and anti-patterns.
Cloud data services at a conceptual level (AWS, Azure, GCP), data pipelines, ML concepts (classification, regression, clustering), AI ethics, and responsible AI principles.

Not Covered

Advanced data science and machine learning engineering at the expert level, including neural network architecture design, deep learning model training, and MLOps pipeline implementation.
Production deployment of ML models, containerized inference endpoints, and CI/CD for machine learning workflows.
Programming language mastery beyond basic SQL query construction; no Python, R, or Scala proficiency is expected.
Vendor-specific cloud platform certifications or deep implementation of AWS, Azure, or GCP data services.
Enterprise-scale data architecture design, distributed systems engineering, and database administration at the DBA level.

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