Associate Big Data Analyst

About the ABDA™ Exam

The ABDA™ certification exam is built on the DASCA Essential Knowledge Framework (DASCA-EKF™) — a globally benchmarked standard defining the foundational competencies, methodologies, and tools required of entry-level data analytics professionals.

The exam assesses readiness across nine key domains, covering business fundamentals, data manipulation and visualization, analytics tools and methods, decision-making, automation, industry-specific applications, and generative AI. Each domain reflects analysts ability to handle, interpret, and communicate insights from large scale datasets effectively.

Designed to validate both conceptual understanding and applied skills, the exam goes beyond tool-specific knowledge to evaluate an analysts ability to generate actionable insights, support decision making, and leverage automation and AI for efficiency. Weightings are calibrated to reflect the evolving responsibilities of a data analyst, from core analytics and reporting to practical applications across business and industry contexts.

The ABDA™ Exam Emphasizes:

• Applied understanding of business analytics and decision making for actionable insights.
• Conceptual fluency in data manipulation, SQL/Python, and analytics tools beyond platforms.
• Effective use of data visualization and storytelling to communicate findings clearly.
• Practical application of advanced analytics, automation, and AI across business and industry contexts.

Examination Coverage Information

• Business Analytics Fundamentals

  15%

  Description & Practical Applications

  Introduces essential business analytics concepts and how they drive organizational decision-making. Covers key analytics terminology, common business metrics (KPIs), types of analysis (descriptive, diagnostic, predictive, prescriptive), and the analytics lifecycle. Includes real-world examples of how analytics solves business problems across departments (marketing, operations, finance, etc.).

  Why It Matters

  Creates the foundation for understanding how data translates to business value. Analysts need to understand the "why" behind analytics work and how their analysis connects to business outcomes. This module ensures analysts can speak the language of business and understand stakeholder needs.

• Data Visualization & Storytelling

  20%

  Description & Practical Applications

  Develops skills in creating effective visualizations and communicating insights. Covers dashboard design principles, choosing appropriate chart types, visualization tools (Tableau, Power BI, Excel, Python Matplotlib), presentation techniques, and storytelling frameworks. Emphasizes making data accessible to non-technical audiences and driving action through insights.

  Why It Matters

  Even perfect analysis is worthless if stakeholders can't understand it. This module ensures analysts can translate complex findings into clear, compelling visuals and narratives that influence decision-makers. Visualization is often the most visible and impactful part of an analyst's work.

• Data Manipulation & SQL/Python

  20%

  Description & Practical Applications

  Focuses on hands-on data preparation using SQL and spreadsheets. Covers writing SQL/Python queries (from basic to advanced), data cleaning techniques, joining and aggregating data, working with date/time data, string manipulation, and creating derived metrics. Includes common SQL patterns for business reporting and analysis.

  Why It Matters

  Data rarely comes in the exact format needed for analysis. This module teaches the everyday skills analysts use to access, clean, and reshape data. SQL remains the most universal language for working with structured data across nearly all business environments.

• Business Analysis Tools & Methods

  15%

  Description & Practical Applications

  Provides practical experience with the everyday tools of business analysis. Covers advanced spreadsheet techniques (Excel/Google Sheets formulas, pivot tables, What-If analysis), reporting tools, A/B testing methodology, cohort analysis, funnel analysis, and financial analysis basics (profit margins, ROI calculations, forecasting).

  Why It Matters

  These are the practical analytical techniques and tools analysts use daily across industries. Analysts need a strong foundation in these universal methods before moving to more specialized or advanced approaches. These skills deliver immediate value in any business setting.

• Data-Driven Decision Making

  10%

  Description & Practical Applications

  Explores frameworks for using data to make and influence business decisions. Covers analytical problem framing, hypothesis testing for business questions, statistical significance (in practical terms), analysis planning, stakeholder management, and requirements gathering. Emphasizes critical thinking and avoiding common analytical pitfalls and biases.

  Why It Matters

  Successful analysts don't just provide data—they drive decisions. This module bridges technical skills with business impact by teaching analysts how to frame problems, structure analysis to answer specific questions, and present findings in ways that lead to action.

• Analytics Platforms & Data Systems

  10%

  Description & Practical Applications

  Provides a practical understanding of the data systems analysts work with. Covers data warehouses like Postgres, MySQL, business intelligence platforms, reporting systems, dashboarding tools, data catalogs, basic ETL concepts, and working with APIs. Focuses on how analysts interact with these systems rather than building them.

  Why It Matters

  Modern analysts need to understand where data lives, how it flows through an organization, and how to access it efficiently. This module ensures analysts can navigate enterprise data ecosystems without needing to be data engineers.

• Advanced Analytics & Automation

  5%

  Description & Practical Applications

  Introduces practical applications of advanced analytics techniques. Covers basics of predictive modeling, segmentation analysis, text analytics, automated reporting, dashboard automation, analytics scripting basics (Python for business use cases), and when to use AI-assisted analytics tools. Focuses on application rather than theory.

  Why It Matters

  As analysts grow, they need efficient ways to handle larger datasets and more complex analysis. This module introduces techniques that extend beyond basic reporting to provide deeper insights and save time through automation, without requiring deep statistical or programming expertise.

• Industry-Specific Analytics

  3%
  Description & Practical Applications

  Explores how analytics varies across industries with popular use cases. Covers industry-specific metrics and KPIs for sectors like retail (inventory turnover, same-store sales), marketing (CAC, LTV), finance (risk metrics, portfolio analysis), healthcare (patient metrics, operational efficiency), and others.

  Why It Matters

  Different industries have unique analytical challenges and metrics. This module helps analysts understand how to adapt their skills to specific business contexts and speak the language of their particular industry.

• Generative AI in Data Analytics

  2%

  Description & Practical Applications

  Introduces practical applications of generative AI for everyday data analytics tasks. Covers using AI tools to automate reporting, generate data summaries, assist with data cleaning, create synthetic data for testing. Includes prompt engineering for analytics, evaluating AI outputs, and integrating AI tools with existing analytics processes.

  Why It Matters

  Generative AI is transforming how analysts work by automating routine tasks and providing new capabilities. This module ensures analysts can leverage these powerful tools to increase productivity, generate new insights, and solve problems that were previously impractical to address with traditional methods.