CompTIA Data+ Training

This course prepares you for the DA0-002 certification exam, covering all official exam domains and their approximate weightings:

Domain 1 Data Concepts and Environments (20%)

• Explain data concepts: database types (relational/non-relational), file extensions (.csv, .xlsx, .json, .txt, .jpg, .dat), data structures (structured/semi-structured/unstructured, tables, schema, dimensional tables), and data types (string, numeric, datetime, spatial, boolean, large objects, GUID/UUID)
• Identify types of data sources: databases, APIs, website data, files, logs, and data repositories (data lakes, lakehouses, marts, silos, warehouses)
• Identify infrastructure concepts: cloud providers (AWS, Azure, Google), cloud/on-prem models (private, public, hybrid), storage types (object, file, local, shared, block), and containerization
• Identify common data analysis tools: coding environments/IDEs, BI software (Tableau, Power BI, Looker), packages/libraries (pandas, tidyverse, Anaconda), programming languages (SAS, Python, R, Scala), and DBMS tools (SQL Server Management Studio, MySQL Workbench, MongoDB Compass, DBeaver, Toad, Azure Data Studio)
• Identify artificial intelligence (AI) concepts: generative AI/LLMs, foundational models, deep learning, natural language processing (NLP), and robotic process automation (RPA) including automated reporting

Domain 2 Data Acquisition and Preparation (22%)

• Given a scenario, use data acquisition methods: data integration, querying (join, concatenate, filter, union, grouping, aggregate, nested queries), basic query optimization (indexing, parameterization, subsets, temporary tables), ETL/ELT, and data collection (surveying, sampling)
• Given a scenario, perform data exploration to identify possible inconsistencies with a data set: missing values, duplication, redundancy, outliers, completeness, and validation
• Given a scenario, perform appropriate data transformation and cleansing techniques: string manipulation (RegEx), conversion, clustering/binning, augmentation, exploding, scaling, standardization, imputation, parsing, merging, appending, derived variables/calculated fields, and deletion

Domain 3 Data Analysis (24%)

• Given a set of requirements, determine the appropriate communication approach for data analysis: mock-ups, accessibility (auditory, visual), technical vs. non-technical audience, level of detail, internal vs. external, user persona type (C-suite vs. individual contributor), sensitive vs. non-sensitive data, and KPIs
• Given a scenario, select the appropriate statistical method or function: basic statistical methods (prescriptive, descriptive, predictive, inferential) and functions/measures (mathematical measures of central tendency and dispersion, logical, date, string)
• Given a scenario, troubleshoot basic issues using the appropriate tool or method: connectivity-related, user-reported, basic SQL code, and corrupted data issues; tools/methods including enable logging, validate data source, and consulting vendor communities/online resources

Domain 4 Visualization and Reporting (20%)

• Given a scenario, use the appropriate visual elements: types (charts, maps, pivot tables, infographics) and design elements (labels, legends, branding, color schemes)
• Given a scenario, use the appropriate delivery or consumption method: executive summary, self-service portal, dashboards (static, dynamic, recurring, ad hoc), and data versioning techniques (snapshot, real-time)
• Given a scenario, troubleshoot issues using report validation techniques: issues (excessive load time, slow refresh rate, large data size, filter not working correctly, stale data, corrupt data) and techniques (data filtering, code/calc/peer review, source validation, data structure changes, monitoring alerts)

Domain 5 Data Governance (14%)

• Explain data management concepts: integration, documentation (data flow diagram, data explainability report, data dictionary, hierarchy structure, data lineage), source of truth, data versioning (snapshots, refresh intervals), and metadata
• Summarize concepts related to data compliance: retention, GDPR, jurisdictional requirements, replication, storage, data ethics, PCI DSS, audit, classification, and incident reporting (data breach, security)
• Compare and contrast data privacy and protection practices: role-based access control, encryption (in transit, at rest), data usage/sharing, NIST, PII, PHI, anonymization, and masking
• Compare and contrast data quality assurance practices: requirement testing, stress test, UAT, source control, unit test, data health check/data drifts, automated data quality monitoring, data profiling/quality metrics, and ISO standards