Data Interpretation for Decision-Making

This module teaches learners to read, question and act on the numbers that cross a modern manager's desk. The focus is on turning dashboards, reports and ad-hoc tables into clear, defensible decisions rather than on building the underlying data pipelines. Learners practise diagnosing what a metric movement actually means, choosing the right chart and spotting classic analytical traps such as Simpson's paradox and vanity KPIs. By the end, learners can walk into a review meeting with a dashboard and lead a numerate discussion of what to do next.

Learning Outcomes

Define and critique common KPIs across sales, marketing, operations and finance.

Compute and interpret percent change, year-on-year and month-on-month growth and compound annual growth rate.

Choose appropriate charts (bar, line, stacked, scatter, heatmap) for a given question and audience.

Diagnose metric movements using segmentation, mix shift and cohort analysis.

Identify Simpson's paradox, survivorship bias and vanity-metric traps in business reports.

Read dashboards and executive summaries, separating signal from noise and seasonality.

Translate numerical findings into a clear recommendation and next action for stakeholders.

Specify a basic data request to an analyst that is answerable, measurable and time-bounded.

Topics Covered

KPI selection and the North Star metric

Percent change, growth rates and CAGR

Year-on-year vs month-on-month comparisons

Chart choice and visual encodings

Dashboards and executive summaries

Segmentation and drill-down analysis

Cohort analysis and retention curves

Funnel analysis and conversion rates

Simpson's paradox and aggregation bias

Survivorship, selection and sampling bias

Benchmarking and target setting

Variance analysis and bridge charts

Correlation vs causation in business data

Reading A/B-test and experiment readouts

Writing an insight: finding, so-what, next step

Skills Developed

Critical reading of charts and tables Metric decomposition and bridge-building Chart selection and layout Spotting data-quality and bias issues Clear written insight communication Spreadsheet-based diagnostic analysis Hypothesis-driven questioning of data Stakeholder-ready storytelling with numbers

Suggested Resources

{'title': 'Storytelling with Data', 'author': 'Cole Nussbaumer Knaflic', 'source': 'Wiley, 2015'}

{'title': 'Thinking with Data', 'author': 'Max Shron', 'source': "O'Reilly Media, 2014"}

{'title': 'The Visual Display of Quantitative Information', 'author': 'Edward R. Tufte', 'source': 'Graphics Press, 2001'}

{'title': 'How Charts Lie', 'author': 'Alberto Cairo', 'source': 'W. W. Norton, 2019'}

{'title': 'Lean Analytics', 'author': 'Alistair Croll and Benjamin Yoskovitz', 'source': "O'Reilly Media, 2013"}

{'title': 'Google Analytics Academy', 'author': 'Google', 'source': 'analytics.google.com/analytics/academy'}

{'title': 'Measure What Matters', 'author': 'John Doerr', 'source': 'Portfolio, 2018'}

Assessment

Assessment uses supplied company dashboards and extracts. Learners complete written insight notes, chart-critique tasks and a capstone in which they diagnose a drop in a core KPI using segmentation and cohort views, then recommend an action.

weekly_mcq_quizzes17%

chart_critique_assignments17%

kpi_diagnosis_problem_sets17%

cohort_and_funnel_mini_project17%

capstone_kpi_investigation17%

final_proctored_exam17%

Practice bank: 200 MCQs · Timed mock & self-paced modes

Start Practice →