• Early- to mid-level data analysts, reporting analysts, business intelligence associates, or business data analysts
• Professionals in IT, business, operations, or domains who want to strengthen their data literacy
• Individuals preparing to sit for the CompTIA Data+ (DA0-001) exam
• Anyone seeking a vendor-neutral credential validating competence in data analytics

• 18–24 months of experience in a reporting, business analyst, or data-related role is recommended by CompTIA
• Exposure to databases, analytical tools, or reporting platforms
• Basic understanding of statistics and data visualization fundamentals

• Identify basic concepts of data schemas and dimensions and understand the differences between common data structures and file formats to build a strong foundation in data concepts and environments.
• Apply data acquisition, cleansing, profiling, and manipulation techniques to enhance data mining skills.
• Use appropriate descriptive statistical methods and summarize types of analysis and critical analysis techniques for effective data analysis.
• Translate business requirements into meaningful visualizations by creating reports or dashboards.
• Summarize key data governance concepts and apply data quality control techniques to ensure accuracy and compliance.

• Database types (relational, non-relational), data warehousing, data marts, lake, OLTP vs OLAP
• Schema models (star, snowflake), dimensions, slowly changing dimensions
• Data types (numeric, categorical, text, images, etc.)
• Data structures & file formats: structured vs unstructured, JSON, XML, CSV, flat files

• Data acquisition techniques (ETL, ELT, APIs, web scraping, sampling)
• Data cleansing, profiling, handling missing or invalid data, outliers
• Manipulation: recoding, merging, concatenation, normalization, aggregation, parsing
• Query optimization, indexing, filtering, sorting, logical / system functions

• Descriptive statistics: measures of central tendency, dispersion, distribution
• Inferential statistics: hypothesis testing, correlation, regression, p-values, chi-square, t-test
• Types of analysis: trend, comparative, exploratory, gap analysis
• Recognition of analytics tools and when to use them

• Translating business requirements into reports/dashboards

• Report/dashboard design: layout, branding, labeling, color, filters, interactivity

• Choosing appropriate visual types: bar, line, scatter, tree map, pie, histogram, heat map

• Differences between static vs dynamic reports

• Delivery mechanisms, audience considerations, interactive filtering

• Governance concepts: roles, policies, compliance, data classification, retention
• Security, privacy, anonymization, de-identification
• Data quality: accuracy, consistency, completeness, validation, profiling
• Master Data Management (MDM) principles
• Audits, data validation controls, rule enforcement