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MAKE YOUR LEARNING VISIBLE

Build something you can walk someone through.

A useful portfolio shows the problem, your choices and the evidence behind the result. These sample briefs help you picture the work.

Explore the examples
YOUR PROJECT WORKSPACE
DATA ANALYTICS · SAMPLE PROJECT

From a spreadsheet
to a story worth sharing.

Campus store · Sales overviewIllustrative data
Net sales₹84,200
Orders248
Period4 weeks
Sample mentor feedback

“The trend is clear. Now check how returns change your net sales calculation.”

BuildReviewImproveExplain
A fictional project and feedback example, not a learner result.
HONEST PROJECT EVIDENCE

Show what you did and how you checked it.

All examples below are illustrative, using synthetic or appropriately licensed inputs. They are not learner testimonials, client engagements or a promise that every programme includes these exact projects.

1

State your own contribution

2

Keep the source and permissions

3

Document reviews and revisions

4

Explain limitations and next steps

One example from every track.

All three AI and Generative AI briefs →Explore the track →
Illustrative project 01

Campus information assistant

Help a student find an answer in a small set of campus documents.

Sample inputs
Write a fictional club handbook and 20 questions, including five that the handbook cannot answer.
Your tasks
Define supported questions, create grounded prompts, record responses and add an explicit 'not found' path.
What to deliver
A conversation prototype, source-linked answer sheet and failure log.
Quality checks
Every factual answer should be traceable to the handbook; test missing, conflicting and misleading questions.
Interview discussion
Explain how you distinguished a fluent answer from a supported answer, and why you limited scope.
All three Machine Learning briefs →Explore the track →
Illustrative project 01

Demand prediction baseline

Estimate next-period demand and compare it with a simple historical rule.

Sample inputs
A synthetic weekly sales series with seasonal variation and documented generation assumptions.
Your tasks
Create a chronological split, build a naive forecast, train a small regression model and compare errors.
What to deliver
A notebook with baseline and model predictions plus an error chart.
Quality checks
Use only information available at prediction time; report MAE and errors during unusual periods.
Interview discussion
Explain why a random split could misrepresent future performance.
All three Data Analytics briefs →Explore the track →
Illustrative project 01

Sales performance dashboard

Help a fictional retailer understand which products and regions changed sales.

Sample inputs
Synthetic orders with order ID, date, region, product, quantity, price and refund status.
Your tasks
Define net sales, clean duplicates, aggregate by period and build filters with a concise findings memo.
What to deliver
A dashboard, calculation dictionary and reconciliation sheet.
Quality checks
A duplicated order must not increase totals; refunded orders and incomplete periods need explicit treatment.
Interview discussion
Explain why growth in revenue may not imply growth in profit.
All three Data Science briefs →Explore the track →
Illustrative project 01

Customer behaviour investigation

Understand how activity differs between new and returning fictional customers.

Sample inputs
Synthetic dated transaction records with a documented customer definition.
Your tasks
Build cohorts, examine missingness, visualise distributions and compare first-month behaviour.
What to deliver
An EDA notebook and a qualified findings memo.
Quality checks
Avoid comparing a full year's returning customers with one week of new customers; document observation windows.
Interview discussion
Explain how cohort definitions and survivorship affect the result.
All three Cloud Computing briefs →Explore the track →
Illustrative project 01

Small website hosting plan

Make a sample site available through a documented hosting setup.

Sample inputs
Your own static site and a test cloud account, or a local simulation before spending.
Your tasks
Draw the request path, configure hosting, restrict administrative access and document deployment and teardown.
What to deliver
An architecture diagram, runbook and cost checklist.
Quality checks
Check HTTPS, permissions and resource removal. A budget alert is not a spending cap.
Interview discussion
Explain your service choice and the difference between availability and backup.
All three DevOps briefs →Explore the track →
Illustrative project 01

Tested application pipeline

Catch a broken change before it reaches a sample deployment.

Sample inputs
A small application you own with a meaningful test and a disposable runner environment.
Your tasks
Create a branch, run checks on changes, deliberately fail a test and prevent release until it passes.
What to deliver
A repository, pipeline file and recorded pass/fail evidence.
Quality checks
Do not expose secrets in logs; verify the failed test genuinely stops deployment.
Interview discussion
Explain why a green pipeline does not prove every production behaviour is correct.
All three Cybersecurity briefs →Explore the track →
Illustrative project 01

Local web security review

Document a small set of security checks on an intentionally vulnerable local training application.

Sample inputs
An isolated training app, synthetic accounts and a written lab scope.
Your tasks
Map assets, inspect access boundaries, record a permitted finding and propose a defensive correction.
What to deliver
A sanitised report with scope, evidence, impact and retest steps.
Quality checks
Keep the app off public networks, use only lab accounts and stop if the test reaches outside scope.
Interview discussion
Explain the difference between an observed weakness and a proven impact.
All three AI Automation briefs →Explore the track →
Illustrative project 01

Enquiry triage workflow

Organise incoming enquiries so a coordinator can decide what to do next.

Sample inputs
Twenty fictional enquiries with track interest, missing fields and repeated requests.
Your tasks
Validate inputs, classify the topic, draft a response and route it for human approval. Simulate delivery only.
What to deliver
A process map, workflow demonstration and exception queue.
Quality checks
Repeated inputs must not create repeated messages. Unclear cases must reach a person.
Interview discussion
Explain why classification can use AI while consent and sending use explicit rules.
All three Business Analytics briefs →Explore the track →
Illustrative project 01

Retail margin review

Help a fictional store understand why higher sales did not produce higher margin.

Sample inputs
Synthetic product sales, purchase costs, discounts and returns.
Your tasks
Define gross margin, compare product mix, quantify discount effects and propose a small pricing experiment.
What to deliver
A KPI workbook and a five-slide management review.
Quality checks
Distinguish gross margin from net profit and state which operating costs are excluded.
Interview discussion
Explain why reducing discounts could hurt volume and how you would measure the tradeoff.

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