Plan end-to-end data science projects.
School of Data & AIData & AIIntermediateIncluded in a Professional Diploma
Data Science Studio
Turn your data science skills into complete portfolio projects.
Apply Python, statistics, visualization, machine learning, storytelling, and project documentation to build end-to-end data science projects that prove what you can do.
Duration
8 weeks - 6-8 hours/week
Project
Plan end-to-end data science projects.
Support
Pricing and enrolment are handled through the Professional Diploma
Overview
A practical Short Course built around a visible project.
Complete end-to-end data science projects that combine problem framing, data cleaning, exploration, statistics, visualization, modeling, evaluation, storytelling, and presentation.
Frame business or product problems as data science questions.
Clean, explore, and prepare real datasets.
Apply statistics and visualization to understand patterns.
Build and evaluate machine learning models.
Interpret results and explain model limitations.
Communicate insights, recommendations, and business value.
Write strong project documentation and case studies.
Present data science work professionally.
Build portfolio-ready data science projects.
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 Plan end-to-end data science projects..
1Phase 1 - Data Science Project DiscoveryTranslate business requests into scoped data science briefs with problem type, target/objective, metrics, assumptions, risks, and deliverables.1 modules5 lessons1 week
Module 1: From Business Problem to Data Science BriefUnderstand the decision, frame the problem, define success metrics, scope the project, and produce the first brief.5 lessons
Lesson 1: Understanding the DecisionLearn how to identify the decision a data science project should support before choosing analytics, rules, or machine learning.85 minarticle6 pages
Welcome and Learning Objectives
Introduce decision-first data science.
8 min
Decision-First Data Science
Explain why the decision comes before the model.
18 min
Prediction vs Explanation
Clarify a critical project framing difference.
20 min
Automation vs Decision Support
Explain the difference between automated decisions and human-supported decisions.
18 min
When Data Science Is Unnecessary
Explain when not to use ML or data science.
18 min
Exercise - Right Approach Review
Students decide whether analytics, rules, or ML is the right approach.
21 min
Lesson 2: Problem FramingConvert vague business requests into clear data science problem statements across regression, classification, clustering, forecasting, recommendation, and ranking workflows.85 minarticle4 pages
Welcome and Learning Objectives
Introduce data science problem framing.
8 min
Common Data Science Framings
Explain major framing options.
24 min
From Vague Request to Clear Statement
Show how to rewrite vague requests.
22 min
Exercise - Data Science Framing Lab
Students convert vague requests into clear data science problem statements.
31 min
Lesson 3: Defining Success MetricsDefine model metrics and business metrics for churn, fraud, demand, customer segmentation, and lead scoring projects.85 minarticle5 pages
Welcome and Learning Objectives
Introduce success metric design.
8 min
Model Metric vs Business Metric
Explain the difference.
20 min
Cost of Wrong Predictions
Teach false positive and false negative thinking.
22 min
Operational Usefulness and Stakeholder Acceptance
Connect metrics to workflow adoption.
18 min
Exercise - Success Metric Design Studio
Students define metrics for common data science projects.
37 min
Lesson 4: Data Science Project ScopeDefine scope, assumptions, constraints, risks, ethics, and delivery timeline for a data science project.80 minarticle4 pages
Welcome and Learning Objectives
Introduce data science project scope.
8 min
Scope, Assumptions and Constraints
Explain scope control.
20 min
Risks and Ethical Concerns
Explain responsible planning.
20 min
Exercise - One-Page Data Science Brief
Students write a data science project brief.
32 min
Lesson 5: Mini Project 1 - Data Science BriefStudents choose a project domain and produce a professional data science brief.100 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
2Phase 2 - Data Audit, EDA and Modeling ReadinessAudit data quality, validate target/features, perform modeling-focused EDA, and package modeling-ready datasets.2 modules9 lessons1–2 weeks
Module 1: Data Understanding and AuditReview datasets, quality, target/feature validity, leakage risk, and modeling-focused EDA.4 lessons
Lesson 1: Data InventoryDocument available datasets, tables/files, data sources, granularity, ownership, and refresh frequency.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
30 min
Practice Activity
Apply the lesson through a guided studio activity.
22 min
Lesson 2: Data Quality AuditAudit missing values, duplicates, invalid categories, date problems, outliers, broken relationships, and inconsistent labels.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 3: Target and Feature ValidationValidate target definition, label quality, feature availability, leakage risks, and historical sufficiency.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 4: Exploratory Data Analysis for ModelingUse distribution, segment, relationship, target imbalance, outlier, and time-pattern analysis to support modeling decisions.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
37 min
Practice Activity
Apply the lesson through a guided studio activity.
30 min
Module 2: Modeling-Ready Dataset PreparationClean, engineer, split, and package datasets for reproducible modeling.5 lessons
Lesson 1: Cleaning for ModelingApply missing data strategy, duplicate handling, type correction, category standardization, and outlier treatment.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 2: Feature Engineering ReviewCreate numerical, categorical, date/time, aggregation, behavioral, and domain features.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 3: Split StrategyChoose train/validation/test, time-based, stratified splits, leakage prevention, and reproducibility.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 4: Baseline Dataset PackagePackage clean data, feature list, target definition, preprocessing plan, and dataset versioning concept.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 5: Milestone Project 1 - Data Audit and Modeling Readiness ReportProduce a modeling readiness report before training final models.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
3Phase 3 - Modeling, Experimentation and EvaluationBuild baselines, candidate models, pipelines, experiment logs, evaluation reports, and model selection memos.2 modules9 lessons1–2 weeks
Module 1: Building Baselines and Candidate ModelsBuild credible baselines, select candidate models, create pipelines, and track experiments.4 lessons
Lesson 1: Baseline StrategyUse simple statistical baselines, business-rule baselines, majority class baselines, and naive forecasting baselines.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
30 min
Practice Activity
Apply the lesson through a guided studio activity.
22 min
Lesson 2: Candidate Model SelectionSelect models based on model families, interpretability, data size, business constraints, and maintenance concerns.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 3: Model PipelinesBuild preprocessing, feature, and model pipelines while avoiding leakage and supporting reproducible experiments.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 4: Experiment TrackingTrack runs, metrics, parameters, dataset versions, and model comparisons.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Module 2: Evaluation and Error AnalysisChoose metrics, validate robustness, analyze errors, and decide the final model.5 lessons
Lesson 1: Choosing Evaluation MetricsChoose regression, classification, forecasting, clustering, and business-aligned metrics.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 2: Cross-Validation and RobustnessUse cross-validation, stratified CV, time-series validation, metric variability, and robustness concerns.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 3: Error AnalysisAnalyze false positives, false negatives, residuals, segment errors, edge cases, and outlier behavior.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
37 min
Practice Activity
Apply the lesson through a guided studio activity.
30 min
Lesson 4: Model Selection DecisionChoose a usable model based on performance, interpretability, cost, complexity, and deployment readiness.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 5: Milestone Project 2 - Modeling and Evaluation ReportProduce the modeling and evaluation report for the studio project.130 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
4Phase 4 - Interpretation, Responsible Use and Business TranslationInterpret models, review risk, translate predictions into action, and prepare stakeholder-ready documentation.2 modules9 lessons1–2 weeks
Module 1: Model InterpretationExplain model behavior, segment performance, limitations, and responsible-use concerns.4 lessons
Lesson 1: Explaining Model BehaviorExplain feature importance, coefficients, tree-based explanations, local/global interpretation, and interpretation limits.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 2: Segment-Level InsightsReview which groups perform differently, affected users, segment errors, and fairness concerns.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 3: Risk and LimitationsWrite data limitations, model limitations, bias risk, drift risk, unstable features, and human oversight needs.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 4: Responsible Data ScienceReview sensitive features, proxy variables, privacy, fairness, misuse of predictions, and ethical review.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Module 2: From Model Output to Business ActionTurn predictions into operational action and communicate results responsibly.5 lessons
Lesson 1: Turning Predictions into DecisionsDesign score thresholds, action rules, recommended interventions, human review, and operational workflow.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 2: Communicating ResultsWrite technical summaries, executive summaries, recommendation memos, visuals, and avoid overclaiming.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 3: Model DocumentationDraft model cards, data cards, assumptions, training process, intended use, and limitations.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 4: Stakeholder PresentationPrepare presentation structure, simple methodology explanation, objections, tradeoffs, and uncertainty.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
37 min
Practice Activity
Apply the lesson through a guided studio activity.
30 min
Lesson 5: Mini Project 2 - Model Interpretation and Business Action PackPackage interpretation, responsible-use, model card, and business action artifacts.110 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
5Phase 5 - Packaging, Portfolio and HandoffPackage the project for portfolio, reproducibility, handoff, case study writing, and interview readiness.2 modules7 lessons1 week
Module 1: Data Science Project PackagingRefactor projects into professional, reproducible, handoff-ready repositories.4 lessons
Lesson 1: Professional Repository StructureStructure README, notebooks, src, raw/processed data, reports, models, requirements, and reproducibility notes.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
30 min
Practice Activity
Apply the lesson through a guided studio activity.
22 min
Lesson 2: ReproducibilityUse environment files, random seeds, notebook order, data paths, rerunnable pipelines, and dependency notes.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 3: Handoff for Deployment or Business UseWrite input/output contracts, batch scoring notes, dashboard handoff, API concepts, monitoring, and retraining notes.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 4: Portfolio Case Study WritingWrite the problem, data, approach, results, recommendation, limitations, and reflection.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Module 2: Interview and Career ReadinessPrepare to explain, defend, review, and improve the data science project in interviews.3 lessons
Lesson 1: Explaining Projects in InterviewsExplain problem framing, model choice, validation, improvement ideas, and business impact.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 2: Data Science Case InterviewsSolve case prompts using framing, metrics, data needed, model options, evaluation, and risks.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 3: Technical Review PreparationPrepare for code, notebook, metric, assumptions, and model limitations review.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
6Phase 6 - Final Capstone and GraduationComplete an end-to-end data science capstone and package final portfolio assets.1 modules3 lessons1–2 weeks
Module 1: End-to-End Data Science CapstoneStudents complete one serious project domain from brief to presentation.3 lessons
Lesson 1: Capstone OptionsChoose a serious data science project domain.55 minarticle1 pages
Choose Your Data Science Capstone
Review approved final capstone options.
55 min
Lesson 2: Final Capstone - End-to-End Data Science CapstoneStudents complete a professional data science capstone from brief to final presentation.180 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
Lesson 3: Graduation Requirements and Portfolio OutcomeClarify final completion requirements and portfolio assets.55 minarticle1 pages
Requirements and Portfolio Checklist
Summarize graduation requirements and portfolio outcomes.
55 min
Tools and skills
Build skill with the tools used in the work.
Plan end-to-end data science projects.Frame business or product problems as data science questions.Clean, explore, and prepare real datasets.Apply statistics and visualization to understand patterns.Build and evaluate machine learning models.Interpret results and explain model limitations.Communicate insights, recommendations, and business value.Write strong project documentation and case studies.Present data science work professionally.Build portfolio-ready data science projects.
Projects and exercises
- Plan end-to-end data science projects.
- Structured exercises
- Portfolio practice
Resources included
- Course resources
- Project guidance
- Learners building practical tech skills
- A willingness to practice consistently
Career relevance
Data Science Studio 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.
It is a project-focused course where learners apply Python, statistics, visualization, and machine learning to complete end-to-end data science projects.
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