Understand the role of statistics in data science and decision-making.
School of Data & AIData & AIBeginner to IntermediateIncluded in a Professional Diploma
Statistics for Data Science
Build the statistical thinking every serious data scientist needs.
Learn how to summarize data, measure uncertainty, test assumptions, interpret patterns, and make stronger decisions using practical statistics for data science.
Duration
6 weeks - 6-8 hours/week
Project
Understand the role of statistics in data science and decision-making.
Support
Pricing and enrolment are handled through the Professional Diploma
Overview
A practical Short Course built around a visible project.
Build the statistical foundation needed to understand data, measure uncertainty, test assumptions, interpret patterns, and prepare for machine learning.
Use descriptive statistics to summarize datasets.
Understand probability, uncertainty, and variation.
Interpret distributions, outliers, and data spread.
Understand sampling and why sample quality matters.
Measure relationships using correlation and basic association.
Understand confidence intervals and statistical significance.
Run and interpret basic hypothesis tests.
Avoid common mistakes when interpreting data.
Use statistics to prepare for machine learning and experimentation.
Course roadmap
What you will work through.
The sequence below is specific to this course. It shows the phases, modules, lessons, and page outlines that move you toward Understand the role of statistics in data science and decision-making..
1Phase 1 - Statistical Thinking for Data ScienceBuild the mindset required for statistics: evidence, uncertainty, sampling, variables, measurement, question design, and bias awareness.1 modules5 lessons1 week
Module 1: Why Statistics MattersUnderstand statistics as decision support and learn how to define better statistical questions before analysis.5 lessons
Lesson 1: What Statistics Is Really ForUnderstand statistics as decision support under uncertainty, not just formulas or academic theory.85 minarticle6 pages
Welcome and Learning Objectives
Introduce the purpose of statistics for data science.
8 min
Statistics as Decision Support
Explain the real purpose of statistics.
18 min
Data vs Evidence
Clarify why data alone is not enough.
18 min
Uncertainty and Misleading Averages
Introduce uncertainty and average traps.
20 min
Statistics in Analytics, ML and AI
Connect statistics to future learning.
18 min
Exercise - Business Claim Evidence Review
Students review business claims and decide whether data supports them.
21 min
Lesson 2: Populations and SamplesUnderstand populations, samples, sampling frames, representative samples, and sampling bias.85 minarticle4 pages
Welcome and Learning Objectives
Introduce populations and samples.
8 min
Population, Sample and Sampling Frame
Explain core sampling terms.
20 min
Representative Samples and Bias
Explain sample representativeness.
22 min
Exercise - Sampling Bias Detective
Students identify sampling problems.
35 min
Lesson 3: Variables and MeasurementClassify numerical, categorical, ordinal, continuous, and discrete variables while recognizing measurement errors.80 minarticle4 pages
Welcome and Learning Objectives
Introduce variables and measurement.
8 min
Types of Variables
Explain variable categories.
22 min
Measurement Errors
Explain measurement risks.
20 min
Exercise - Variable Classification Lab
Students classify variables across domains.
30 min
Lesson 4: Statistical QuestionsLearn how to rewrite vague business questions into descriptive, comparative, relationship, prediction, and causal statistical questions.85 minarticle4 pages
Welcome and Learning Objectives
Introduce statistical question design.
8 min
Types of Statistical Questions
Explain major question types.
24 min
From Vague to Statistical
Show how to rewrite questions.
22 min
Exercise - Statistical Question Rewrite Lab
Students rewrite vague business questions.
31 min
Lesson 5: Mini Project 1 - Statistical Question DesignStudents choose a dataset and design statistical questions with variable classification, expected analysis approach, risks, and bias notes.90 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
2Phase 2 - Descriptive Statistics and DistributionsSummarize data correctly, understand distribution shapes, identify outliers, and compare segments.2 modules9 lessons1–2 weeks
Module 1: Summarizing Data CorrectlyUse measures of center, spread, percentiles, quantiles, and distribution shape to summarize data responsibly.4 lessons
Lesson 1: Measures of CenterUse mean, median, and mode while understanding when each is useful or misleading.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
27 min
Practice Activity
Apply the lesson through a guided statistics exercise.
20 min
Lesson 2: Measures of SpreadUse range, variance, standard deviation, interquartile range, and coefficient of variation.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 3: Percentiles and QuantilesUse percentiles, quartiles, box plots, and outlier detection.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 4: Shape of DataInterpret skewness, symmetry, long tails, and multimodal distributions.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Module 2: Working with DistributionsBuild and interpret common distributions used in data science and business decision-making.5 lessons
Lesson 1: Frequency DistributionsBuild frequency tables, histograms, density views, and distribution comparisons.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
27 min
Practice Activity
Apply the lesson through a guided statistics exercise.
20 min
Lesson 2: Normal DistributionUnderstand bell curves, standard deviation rule, z-scores, and why normality matters.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 3: Common Data Science DistributionsUnderstand binomial, Poisson, exponential, and uniform distributions through business examples.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 4: Transforming DataUse log transformation, scaling concepts, standardization, and why transformations help analysis and ML.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 5: Milestone Project 1 - Descriptive Statistics ReportProduce a descriptive statistics report for a selected dataset.110 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
3Phase 3 - Probability for Data ScienceUse probability to reason about uncertainty, evidence, risk, expected value, and business tradeoffs.1 modules5 lessons1 week
Module 1: Probability FundamentalsUnderstand probability, conditional probability, Bayes intuition, expected value, and risk.5 lessons
Lesson 1: Understanding ProbabilityInterpret probability as uncertainty using events, outcomes, complements, and mutually exclusive events.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
27 min
Practice Activity
Apply the lesson through a guided statistics exercise.
20 min
Lesson 2: Conditional ProbabilityUse conditional probability, dependence, independence, and decision-making examples.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 3: Bayes Rule IntuitionUnderstand prior probability, new evidence, updated belief, medical testing, and fraud detection examples.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 4: Expected Value and RiskUse expected value, risk, decision-making under uncertainty, and business tradeoffs.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 5: Mini Project 2 - Probability Decision CaseSolve a business decision problem using probability and expected value.90 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
4Phase 4 - Sampling, Estimation and ConfidenceUse sampling, sampling distributions, confidence intervals, and interpretation discipline for reliable estimation.1 modules5 lessons1 week
Module 1: Sampling and EstimationDesign sampling approaches, simulate sampling variability, build confidence intervals, and avoid common misinterpretations.5 lessons
Lesson 1: Sampling MethodsUse random sampling, stratified sampling, convenience sampling, sampling bias, and sample size intuition.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 2: Sampling DistributionsUnderstand sample statistics, sampling variability, Central Limit Theorem intuition, and why larger samples help.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
35 min
Practice Activity
Apply the lesson through a guided statistics exercise.
27 min
Lesson 3: Confidence IntervalsBuild confidence intervals and interpret estimates, interval width, confidence level, and margin of error.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
35 min
Practice Activity
Apply the lesson through a guided statistics exercise.
27 min
Lesson 4: Common MisinterpretationsAvoid small sample traps, selection bias, survivorship bias, and wrong confidence interval claims.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 5: Milestone Project 2 - Sampling and Confidence ReportProduce a sampling and confidence report for a business problem.100 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
5Phase 5 - Hypothesis TestingTest business claims using hypothesis testing, p-values, t-tests, proportion tests, chi-square tests, effect size, and business judgment.1 modules7 lessons1–2 weeks
Module 1: Testing Claims with DataWrite hypotheses, choose tests, interpret p-values properly, and turn statistical results into business recommendations.7 lessons
Lesson 1: Hypothesis Testing IntuitionUnderstand null hypothesis, alternative hypothesis, evidence, statistical significance, and practical significance.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 2: P-values Explained ProperlyInterpret what p-values mean, what they do not mean, thresholds, and misuse of p-values.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 3: One-Sample and Two-Sample TestsUse t-tests, comparing means, before/after analysis, independent vs paired samples.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
35 min
Practice Activity
Apply the lesson through a guided statistics exercise.
27 min
Lesson 4: Tests for ProportionsCompare conversion rates, success rates, and difference in proportions.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
35 min
Practice Activity
Apply the lesson through a guided statistics exercise.
27 min
Lesson 5: Chi-Square TestsAnalyze categorical relationships using independence testing and contingency tables.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 6: Statistical vs Business DecisionsBalance significance, impact, effect size, confidence, cost of wrong decisions, and decision thresholds.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 7: Milestone Project 3 - Business Hypothesis Testing ProjectTest several business claims and produce statistical interpretations and recommendations.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
6Phase 6 - Relationships, Correlation and Regression IntuitionUnderstand relationships, correlation, causation, regression intuition, confounding, and ML readiness.1 modules6 lessons1 week
Module 1: Understanding RelationshipsAnalyze relationships between variables and prepare for regression and machine learning concepts.6 lessons
Lesson 1: CorrelationUnderstand positive, negative, no correlation, Pearson, Spearman, and correlation limitations.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 2: Correlation vs CausationUnderstand confounding variables, reverse causality, spurious correlations, and causal thinking.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 3: Simple Linear Regression IntuitionUnderstand outcome variable, predictor variable, slope, intercept, residuals, and error.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
35 min
Practice Activity
Apply the lesson through a guided statistics exercise.
27 min
Lesson 4: Multiple Regression ConceptsUnderstand multiple predictors, control variables, coefficients, interpretation, and assumptions at a high level.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
35 min
Practice Activity
Apply the lesson through a guided statistics exercise.
27 min
Lesson 5: Preparing for Machine LearningUnderstand features, target variables, prediction vs explanation, training data, and evaluation intuition.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 6: Mini Project 3 - Relationship Analysis ReportProduce a relationship analysis report with correlation, confounding discussion, regression interpretation, recommendation, and ML readiness note.100 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
7Phase 7 - Experiments and A/B TestingDesign, analyze, interpret, and audit experiments and A/B tests.1 modules6 lessons1 week
Module 1: Experiment DesignUnderstand observational data, experiments, A/B tests, sample size intuition, result interpretation, and experiment pitfalls.6 lessons
Lesson 1: Why Experiments MatterDifferentiate observational data, controlled experiments, random assignment, treatment, and control groups.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
27 min
Practice Activity
Apply the lesson through a guided statistics exercise.
20 min
Lesson 2: A/B Testing FundamentalsDesign A/B tests with control group, treatment group, primary metric, secondary metrics, and guardrail metrics.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 3: Sample Size and Test DurationUnderstand minimum detectable effect, statistical power intuition, early stopping, and seasonality risks.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 4: Interpreting Experiment ResultsInterpret lift, confidence intervals, statistical significance, business impact, and segment effects.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
35 min
Practice Activity
Apply the lesson through a guided statistics exercise.
27 min
Lesson 5: Experiment PitfallsAudit peeking, multiple comparisons, biased assignment, novelty effect, and misleading dashboards.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 6: Milestone Project 4 - A/B Test AnalysisProduce an A/B test analysis with design, hypotheses, metrics, results, recommendation, and risks.110 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
8Phase 8 - Statistical Communication and Portfolio ReadinessCommunicate uncertainty, structure statistical reports, visualize findings, review quality, and complete the final capstone.2 modules6 lessons1–2 weeks
Module 1: Communicating Statistical FindingsExplain statistical findings clearly to non-technical stakeholders.4 lessons
Lesson 1: Explaining Uncertainty SimplyExplain uncertainty to non-technical stakeholders with confidence, without overclaiming or jargon.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
27 min
Practice Activity
Apply the lesson through a guided statistics exercise.
20 min
Lesson 2: Statistical ReportsStructure reports around problem, data, method, results, interpretation, recommendation, and limitations.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 3: Visualizing Statistical FindingsUse distribution charts, box plots, error bars, confidence interval plots, and experiment result charts.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 4: Quality Checklist for Statistical AnalysisReview data quality, assumptions, sample size, test choice, interpretation, and limitations.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Module 2: Applied Statistics CapstoneComplete a final applied statistics project that supports a business decision with statistical evidence.2 lessons
Lesson 1: Final Capstone - Applied Statistics for Data Science CapstoneStudents choose a domain and support a business decision using statistical evidence.160 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
Lesson 2: Graduation Requirements and Portfolio OutcomeClarify completion requirements and portfolio outputs.45 minarticle1 pages
Requirements and Portfolio Checklist
Summarize graduation requirements and portfolio assets.
45 min
Tools and skills
Build skill with the tools used in the work.
Understand the role of statistics in data science and decision-making.Use descriptive statistics to summarize datasets.Understand probability, uncertainty, and variation.Interpret distributions, outliers, and data spread.Understand sampling and why sample quality matters.Measure relationships using correlation and basic association.Understand confidence intervals and statistical significance.Run and interpret basic hypothesis tests.Avoid common mistakes when interpreting data.Use statistics to prepare for machine learning and experimentation.
Projects and exercises
- Understand the role of statistics in data science and decision-making.
- Structured exercises
- Portfolio practice
Resources included
- Course resources
- Project guidance
- Learners building practical tech skills
- A willingness to practice consistently
Career relevance
Statistics for Data Science supports practical career readiness.
Related Professional Diploma
View Professional DiplomaData Science & Machine Learning
Build practical data science and machine learning skills by learning Python, statistics, data preparation, model training, evaluation, interpretation, and end-to-end data science projects.
FAQ
Questions about this Short Course.
Short Course answers about scope, projects, support, and next steps.
No. The course focuses on practical statistical thinking for data science. You will learn the concepts step by step using examples and datasets.
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