Subject Overview

Statistics for Analytics develops the statistical literacy and practical skills required to collect, summarize, analyze, interpret and communicate business data. The subject combines foundational theory with spreadsheet and statistical-software applications, managerial decision-making, responsible analytics and contemporary AI-enabled practices.

Learning Goal

By completing the subject, students will be able to select and apply appropriate descriptive and inferential statistical methods, evaluate uncertainty, build and validate basic analytical models, translate results into business decisions, and manage statistical work ethically and professionally.

Syllabus

1. Statistical Thinking for Business Analytics

Learning Objective: Explain populations, samples, parameters, statistics, variables, cases, uncertainty and the roles of descriptive, inferential and predictive analytics; frame business problems as answerable statistical questions.

Category: foundation

2. Data Types, Measurement and Data Quality

Learning Objective: Classify categorical and numerical data, measurement scales, structured and unstructured sources, and cross-sectional and time-series data; assess validity, reliability, bias, missingness, outliers and data quality.

Category: foundation

3. Data Collection, Sampling and Experimental Design

Learning Objective: Apply probability and non-probability sampling, determine practical sample requirements, recognize sampling and non-sampling errors, and distinguish observational studies, surveys, experiments, control groups, randomization and confounding.

Category: core

4. Data Preparation and Exploratory Data Analysis

Learning Objective: Clean, code, transform and integrate data; use frequency tables, cross-tabulations, visualizations and exploratory techniques to identify distributions, anomalies, patterns and relationships without overstating conclusions.

Category: application

5. Descriptive Statistics and Business Reporting

Learning Objective: Calculate and interpret measures of location, dispersion, position, shape and association, including mean, median, variance, standard deviation, percentiles, skewness, covariance and correlation; create decision-focused statistical summaries and dashboards.

Category: core

6. Probability Concepts and Business Risk

Learning Objective: Use probability rules, conditional probability, independence, Bayes’ theorem, counting methods and expected value to evaluate uncertainty, update evidence and support risk-based business decisions.

Category: core

7. Random Variables and Probability Distributions

Learning Objective: Model discrete and continuous outcomes using Bernoulli, binomial, Poisson, uniform, normal and exponential distributions; calculate probabilities and expected outcomes and select suitable distributions for business contexts.

Category: core

8. Sampling Distributions and Statistical Estimation

Learning Objective: Explain the central limit theorem, standard error and sampling variability; construct and interpret point estimates, confidence intervals and margins of error for means, proportions and differences.

Category: core

9. Hypothesis Testing and Statistical Decision-Making

Learning Objective: Formulate null and alternative hypotheses; apply one- and two-sample tests for means and proportions; interpret p-values, significance levels, confidence intervals, Type I and Type II errors, statistical power and practical significance.

Category: core

10. Categorical Data Analysis and Analysis of Variance

Learning Objective: Use chi-square tests for goodness-of-fit and independence and one-way ANOVA for comparing multiple means; check assumptions, interpret post-hoc comparisons and translate findings into business actions.

Category: application

11. Correlation and Linear Regression

Learning Objective: Measure association and build simple and multiple linear regression models; interpret coefficients, goodness-of-fit and predictions; assess assumptions, residuals, multicollinearity, influential observations and the distinction between correlation and causation.

Category: core

12. Time-Series Analysis and Forecasting

Learning Objective: Analyze trend, seasonality, cyclical movement and noise; apply moving averages, exponential smoothing and basic trend models; evaluate forecasts using error measures and communicate forecast uncertainty for planning.

Category: application

13. Statistical Software, Reproducibility and AI-Assisted Analytics

Learning Objective: Use spreadsheets and introductory statistical tools such as R, Python or business analytics platforms to prepare data, run analyses and visualize results; document reproducible workflows and critically verify AI-generated code, methods and interpretations.

Category: contemporary

14. Responsible Statistical Practice and Emerging Developments

Learning Objective: Address privacy, consent, security, fairness, transparency, data governance, model risk, misleading visualization and reproducibility; examine real-time analytics, automated experimentation, synthetic data, causal analytics and human oversight in AI-enabled decision systems.

Category: strategic