Electives
Complete exactly two electives. Choose the skills you want to develop, check their prerequisites, and complete the work in both chosen sections.
Data Visualization with Seaborn and Plotly
Before you start
Build clear static and interactive explanations of your cleaned dataset.
Prerequisites: Data Foundations descriptive statistics, cleaning, and basic visualization.
Review Data Foundations before starting.
Learning resources
Intermediate Data Visualization with Seaborn — DataCamp
Elective resource. Complete the plotting lessons and exercises relevant to your cleaned dataset. Full course access requires a subscription.
Introduction to Data Visualization with Plotly — DataCamp
Elective resource. Practice interactive charts and explain chart choices. Full course access requires a subscription.
Practice
Create static and interactive visualizations of a cleaned dataset and explain your chart choices.
Finish this module
Submit runnable work and a short explanation demonstrating this outcome: Create static and interactive visualizations of a cleaned dataset and explain your chart choices. Review the work, fix a relevant failure, and revise incomplete evidence.
Workload
Use the provisional estimates where supplied. Other workloads remain pending pilot. Include implementation, debugging, review, and revision.
GitHub Actions and CI
Before you start
Automate tests and investigate failed workflow runs.
Prerequisites: Git/GitHub workflow and a runnable Python project.
Review Git and GitHub and environment setup before starting.
Learning resources
Hello GitHub Actions — GitHub Skills
Elective resource. Complete this introductory workflow exercise first.
Test with Actions — GitHub Skills
Elective resource. Complete after Hello GitHub Actions; configure and inspect automated test runs.
Practice
Configure automated tests for a Python repository and demonstrate a failed check followed by a successful fix.
Finish this module
Submit runnable work and a short explanation demonstrating this outcome: Configure automated tests for a Python repository and demonstrate a failed check followed by a successful fix. Review the work, fix a relevant failure, and revise incomplete evidence.
Workload
Use the provisional estimates where supplied. Other workloads remain pending pilot. Include implementation, debugging, review, and revision.
Advanced SQL
Before you start
Develop stronger analytical queries and investigate query efficiency.
Prerequisites: Data Foundations SQL, including joins, CTEs, and basic window functions.
Review SQL before starting.
Learning resources
Advanced SQL — Kaggle Learn
Elective resource. Complete the lessons and exercises; apply analytic functions and query-efficiency ideas.
Practice
Use analytic functions to answer questions across related rows and explain choices that improve query efficiency.
Finish this module
Submit runnable work and a short explanation demonstrating this outcome: Use analytic functions to answer questions across related rows and explain choices that improve query efficiency. Review the work, fix a relevant failure, and revise incomplete evidence.
Workload
Use the provisional estimates where supplied. Other workloads remain pending pilot. Include implementation, debugging, review, and revision.
Bash Scripting and Automation
Before you start
Turn repeated terminal commands into scripts using variables, arguments, conditions, loops, pipes, and redirection.
Prerequisites: Terminal commands and a working Python environment.
Review Terminal and environment setup before starting.
Learning resources
LearnShell — Interactive Shell Tutorial
Elective resource. Study variables, arguments, conditions, and loops; complete the corresponding interactive exercises.
Software Carpentry: Pipes and Filters
Elective resource. Complete this lesson and its exercises.
Software Carpentry: Loops
Elective resource. Complete this lesson and its exercises.
Software Carpentry: Shell Scripts
Elective resource. Complete this lesson and adapt a script to your own workflow.
Practice
Create a script that accepts input arguments, processes multiple files, and runs an existing Python program with clear usage instructions.
Finish this module
Submit runnable work and a short explanation demonstrating this outcome: Create a script that accepts input arguments, processes multiple files, and runs an existing Python program with clear usage instructions. Review the work, fix a relevant failure, and revise incomplete evidence.
Workload
Use the provisional estimates where supplied. Other workloads remain pending pilot. Include implementation, debugging, review, and revision.
Algorithms and Problem Solving
Before you start
Go beyond the required kit by choosing algorithms deliberately and comparing alternatives.
Prerequisites: The required HackerRank preparation kit and debugging practice.
Review Required algorithms and debugging before starting.
Learning resources
Grokking Algorithms, Second Edition — Aditya Y. Bhargava
Elective resource. Study the topics used in your chosen problems. This is a paid book.
HackerRank Algorithms
Elective resource. Select problems beyond the full required preparation kit.
Practice
Explain why a solution works, analyze its time and space complexity, and compare alternative approaches.
Finish this module
Submit runnable work and a short explanation demonstrating this outcome: Explain why a solution works, analyze its time and space complexity, and compare alternative approaches. Review the work, fix a relevant failure, and revise incomplete evidence.
Workload
Use the provisional estimates where supplied. Other workloads remain pending pilot. Include implementation, debugging, review, and revision.
Advanced Statistical Inference
Before you start
Develop advanced inference after the required Probability & Statistics module.
Prerequisite: complete the probability and statistics section of Math for AI.
This elective covers Units 13–16 together. Complete all four units to finish the module.
Unit 13 — Two-Sample Inference for the Difference Between Groups
Coverage:
- Comparing two population proportions
- Comparing two population means
- Confidence intervals for differences
- Two-sample significance tests
- Paired-data inference
- Interpreting group differences
Unit 14 — Inference for Categorical Data
Coverage:
- Chi-square goodness-of-fit tests
- Chi-square tests for homogeneity
- Chi-square tests for independence
- Expected counts
- Test conditions
- Interpreting chi-square results
Unit 15 — Advanced Regression
Coverage:
- Inference for regression slope
- Confidence intervals for slope
- Significance tests for relationships
- Residual analysis
- Transforming nonlinear relationships
- Interpreting regression evidence
Unit 16 — Analysis of Variance
Coverage:
- Comparing more than two group means
- Between-group and within-group variability
- F-statistics
- ANOVA hypotheses
- Conditions for ANOVA
- Interpreting ANOVA results
Complete this elective
To complete Advanced Statistical Inference, you must:
- Complete all four units: 13, 14, 15, and 16.
- Pass each corresponding unit test with at least 80%.
- Pass a separate elective verification assessment.
- Correctly select and interpret an appropriate inference method for a given problem.
Classification
| Khan Academy units | Status |
|---|---|
| 1–5 | Required core |
| 6 Study Design | Excluded |
| 7–12 | Required core |
| 13–16 | One elective |
Khan Academy Statistics and Probability
Elective resource. Complete Units 13–16 together, pass every unit test with at least 80%, and pass the separate elective verification.
Workload
Workload estimate pending pilot. Include practice, debugging, review, and revision in your study time.
Finish this module
Complete two sections and submit the evidence required by each. Keep their results and explanations separate, even when both use the same project. Revise incomplete work after review.
