BAN300

Business Analytics

Work through a business decision from the first question to the final recommendation. Eight chapters cover data quality, charts, probability, regression, classification, optimization, and simulation, with fictional cases and calculations you can follow by hand.

  • 8 chapters
  • ~73 min of reading
  • 24 learning objectives
  • 24 practice questions

Course overview

The course runs to 8 chapters and about 73 minutes of reading. Every chapter is self-contained: read it in one sitting, work the example on paper, then check yourself against the practice solutions before moving on.

The learning path below shows how the chapters build on one another, and each chapter card lists what you should be able to do once you have finished it.

How each chapter works

  1. Objectives — what you should be able to do afterwards.
  2. Reading — short sections that build the idea in plain language.
  3. Worked example — a full problem solved step by step.
  4. Practice — questions with solutions you can expand once you have tried them.
  5. Takeaway — the one sentence worth remembering.

Learning path

Chapters build on each other. This is the arc from first principles to the end of the course.

  1. Framing the Decision and Choosing KPIs

    Start from a decision with named options and a stated currency, decompose the headline metric until each branch has an owner, and compare initiatives on net effect in steady state rather than on the apparent size of the lever they pull.

  2. Data Quality and the Audit Before the Analysis

    Audit before you analyse, using counts across completeness, uniqueness, validity, consistency, and timeliness, and correct errors in both directions, because fixing only the defects that are easy to find can leave a metric further from the truth than not cleaning at all.

  3. Visual Exploration and Descriptive Summaries

    Match the summary to the shape of the data, report spread alongside centre, use the quartile fence as a trigger for investigation rather than deletion, and plot at the level where the decision is made so that mix shifts cannot hide inside an average.

  4. Probability, Expected Value, and Decision Trees

    Fold a tree back from right to left in one currency with sunk costs excluded, use a joint probability table to update beliefs after an imperfect test, and remember that information is worth only what it changes, capped by the value of perfect information.

  5. Regression for Prediction and Interpretation

    Fit the line, then report the slope in business units with its confidence interval and the residual standard error, and always convert the slope into money against the cost of the input, because a highly significant effect can still be far too small to pay.

  6. Classification and the Economics of Evaluation

    Report the full confusion matrix with prevalence and the majority-class baseline, pick metrics that match the cost of each error type, and set the threshold where the marginal precision of the next block of cases stops clearing the breakeven implied by the economics of acting.

  7. Optimization, Shadow Prices, and Sensitivity

    Formulate with explicit units and contribution as the objective, find the optimum at a corner and verify it against the dual, and treat shadow prices as buy-or-not prices for capacity that are valid only inside a stated range.

  8. Simulation Capstone: Deciding Under Uncertainty

    Push the whole distribution through the model rather than the average input, use the critical ratio for one-shot stocking decisions and verify it against a direct expected profit table, and always report the downside and the assumptions next to the expected value, because the option with the best mean often has the worst floor.

Chapters (8)

Open any chapter to see its objectives, reading, worked example, and practice questions.

  1. Chapter 1: Framing the Decision and Choosing KPIs

    • Convert a vague business goal into a decision with named options and a measurable outcome
    • Build a KPI tree that decomposes a headline metric into drivers a team can actually move
    • Compare candidate initiatives on net effect rather than on the size of the driver they touch

    3 sections · ~8 min read · 3 practice questions

  2. Chapter 2: Data Quality and the Audit Before the Analysis

    • Run a structured quality audit across completeness, uniqueness, validity, consistency, and timeliness
    • Quantify how duplicates, test records, and missing rows distort a headline ratio
    • Recognise that partial cleaning can move a metric further from the truth than no cleaning at all

    3 sections · ~7 min read · 3 practice questions

  3. Chapter 3: Visual Exploration and Descriptive Summaries

    • Choose summary statistics that match the shape of a distribution rather than defaulting to the mean
    • Apply the quartile fence rule to identify outliers and decide what to do with them
    • Diagnose the common ways a chart misleads, including truncated axes and hidden aggregation

    3 sections · ~8 min read · 3 practice questions

  4. Chapter 4: Probability, Expected Value, and Decision Trees

    • Build a decision tree with decision nodes, chance nodes, and payoffs stated in a single currency
    • Update probabilities with Bayes rule after an imperfect test and fold the tree back to an expected value
    • Compute the value of information and the value of perfect information, and know when expected value is the wrong criterion

    3 sections · ~9 min read · 3 practice questions

  5. Chapter 5: Regression for Prediction and Interpretation

    • Fit a simple linear regression by hand and interpret the slope in business units
    • Compute the residual standard error, the standard error of the slope, and a confidence interval
    • Separate statistical significance from economic significance and state the limits of extrapolation

    3 sections · ~9 min read · 3 practice questions

  6. Chapter 6: Classification and the Economics of Evaluation

    • Build a confusion matrix and compute precision, recall, specificity, and F1 correctly
    • Explain why accuracy is misleading for rare events and choose a metric that matches the cost structure
    • Set a decision threshold by marginal economics rather than by convention

    3 sections · ~9 min read · 3 practice questions

  7. Chapter 7: Optimization, Shadow Prices, and Sensitivity

    • Formulate a constrained allocation problem with a stated objective, decision variables, and units on every constraint
    • Solve a two-variable linear program by evaluating corner points and verify the answer using the dual
    • Interpret shadow prices and their valid ranges as buy-or-not decisions about capacity

    3 sections · ~11 min read · 3 practice questions

  8. Chapter 8: Simulation Capstone: Deciding Under Uncertainty

    • Model a decision whose payoff depends on an uncertain quantity and evaluate options across the full outcome distribution
    • Apply the critical ratio rule to a single-period stocking decision and verify it by direct expected value calculation
    • Size a Monte Carlo run for a target precision and report risk alongside the expected value

    4 sections · ~12 min read · 3 practice questions