Data Analytics Professional

Domains and learning objectives

A domain’s weighting is its share of the discipline, and the share of the examination paper drawn from that domain. The study material and the examination questions are written to the objectives.

1. Domain 1

   Statistical Foundations for Analysts

   20%

   • 1.1Calculate and interpret measures of central tendency, dispersion and position, selecting the measure appropriate to the data's shape and scale.
   • 1.2Distinguish between population and sample, parameter and statistic, and correlation and causation in described business scenarios.
   • 1.3Interpret probability statements, distributions and expected values as they arise in everyday analytical questions.
   • 1.4Evaluate the reliability of a sample-based conclusion using confidence intervals, margin of error and sample size reasoning.
   • 1.5Select the appropriate basic significance test for a described comparison and interpret its result, including what a p-value does and does not say.
   • 1.6Identify common statistical traps in analyses, including survivorship bias, Simpson's paradox, regression to the mean and misuse of averages.

2. Domain 2

   Preparing and Structuring Data

   20%

   • 2.1Identify data quality defects in a described or shown dataset, including duplicates, inconsistent categories, invalid values, and mixed data types.
   • 2.2Select the appropriate cleaning action for a defect, weighing deletion, correction, imputation and flagging, and state its effect on later analysis.
   • 2.3Distinguish between wide and long data layouts and select the reshaping needed for a stated analysis or chart.
   • 2.4Interpret relationships between tables, including primary and foreign keys, one-to-many and many-to-many structures, and the effect of granularity on aggregation.
   • 2.5Evaluate a described data collection or combination step for bias, leakage of meaning, or loss of information, including date, text and unit handling issues.
   • 2.6Select documentation and reproducibility practices that let another analyst retrace a preparation workflow.

3. Domain 3

   Analysis with Spreadsheets and SQL Concepts

   20%

   • 3.1Select the correct spreadsheet function or function combination (lookup, conditional aggregate, logical, text, date) for a described analytical task.
   • 3.2Interpret what a given formula or SQL query returns, including its filtering, grouping and calculation logic.
   • 3.3Distinguish between SQL clauses and their spreadsheet equivalents, including filtering rows versus filtering groups and joining versus looking up.
   • 3.4Calculate summary results that a described pivot table or GROUP BY query would produce from a small dataset.
   • 3.5Evaluate an analytical approach for correctness and efficiency, including common formula and query errors such as wrong join type, absolute versus relative references, and aggregation before filtering.
   • 3.6Select appropriate verification steps for a formula, query or AI-generated draft of either, before its output is used in a decision.

4. Domain 4

   Visualisation and Dashboards

   15%

   • 4.1Select the chart type that best answers a stated analytical question, given the data types and comparison involved.
   • 4.2Identify design faults that distort or obscure data, including truncated axes, dual axes misuse, 3D effects, overplotting and rainbow category colouring.
   • 4.3Evaluate a described dashboard against its audience and decision purpose, including layout hierarchy, filter design and refresh expectations.
   • 4.4Interpret charts accurately, including log scales, stacked and 100 percent stacked bars, box plots and heat maps.
   • 4.5Select accessibility and labelling practices that make a visual readable for colour-blind viewers and international audiences.

5. Domain 5

   Communicating Insight

   10%

   • 5.1Select the framing of a finding that is accurate, decision-relevant and appropriately caveated for a stated audience.
   • 5.2Distinguish between observation, interpretation and recommendation in analytical statements.
   • 5.3Evaluate a written analytical claim for overreach, including generalising beyond the data, hiding uncertainty and burying assumptions.
   • 5.4Interpret stakeholder questions to identify the underlying decision and the analysis actually required.
   • 5.5Select an appropriate structure for an analytical narrative, including headline-first summaries and the role of appendices and reproducibility notes.

6. Domain 6

   Introduction to Predictive Methods

   15%

   • 6.1Distinguish between descriptive, diagnostic, predictive and prescriptive questions, and between supervised and unsupervised approaches.
   • 6.2Interpret the output of a simple linear regression, including slope, intercept, R-squared and residual patterns, in business terms.
   • 6.3Select an appropriate baseline forecasting approach for a described time series, including naive, moving average and trend-with-seasonality reasoning.
   • 6.4Calculate and interpret basic evaluation measures, including forecast error measures and classification accuracy, precision and recall from a small confusion matrix.
   • 6.5Identify overfitting, data leakage and unrepresentative training data as causes of models that fail after deployment.
   • 6.6Evaluate a described predictive claim or AI-generated model output for plausibility, fairness concerns and fitness for the stated decision.