DataCareerHubLearn · Practice · Progress
Explore learning
DataCareerHub
Explore learning
Learning paths/Applied practice
Build on Foundations · Learn by doing

Applied Data Analysis.

Ask a useful question. Work through the evidence. Deliver an answer you can explain.

6 published modules36 published lessons32h 55m suggested study
Explore the complete route →Your workspace ↗
What you will build

One connected
evidence trail.

A decision brief and metric contract. Checked exploration and Python analysis. An uncertainty and sensitivity review. A clear report and capstone handover.

Every lesson combines an explanation, an exercise, an explained solution and a practice knowledge check. Download the files, work locally and compare the reference after attempting your own answer.

Your complete route

Build the skill.
Then connect the work.

Review Foundations ↗
01Free introduction

From business questions to an analysis plan

Turn an unclear stakeholder request into a neutral question, a metric contract and a reproducible analysis plan.

5 published lessons · 4h 15m suggested

You will produce
A neutral decision brief

Overview & practice files ↗
View the 5 lesson topics
  1. Turn a vague request into a decision brief
  2. Define scope, grain and fair comparison windows
  3. Specify metrics, denominators and guardrails
  4. Request the evidence and plan quality checks
  5. Write and review a defensible analysis plan
02Free introduction

Exploring data and finding useful patterns

Profile the evidence, inspect distributions, compare groups and explain useful patterns without mistaking them for causes.

6 published lessons · 5h 10m suggested

You will produce
A source profile and exploration log

Overview & practice files ↗
View the 6 lesson topics
  1. Profile the extract before looking for patterns
  2. Understand distributions instead of relying on one average
  3. Choose charts that reveal rather than manufacture patterns
  4. Compare groups and separate composition from performance
  5. Investigate missingness and alternative explanations
  6. Turn explored patterns into an evidence note
03Free · verified account

Python for practical data analysis

Build a checked pandas workflow: load and profile data, derive measures, group safely, validate joins and explain a reproducible result.

7 published lessons · 6h 30m suggested

You will produce
A reproducible local Python workspace

View access requirements ↗
View the 7 lesson topics
  1. Set up a small, reproducible Python workspace
  2. Load and profile a DataFrame without hiding missing values
  3. Select rows and derive checked analytical columns
  4. Group and aggregate with the right denominators
  5. Combine a lookup without multiplying tickets
  6. Validate the pipeline with meaningful boundary tests
  7. Deliver a reproducible analysis and clear explanation
04Free · verified account

Statistical thinking and uncertainty

Compare effect sizes, understand model-based intervals, test sensitivity and communicate what the evidence can support.

6 published lessons · 5h 30m suggested

You will produce
A population map and assumptions register

View access requirements ↗
View the 6 lesson topics
  1. Define the population before quantifying uncertainty
  2. Compare effects with clear units and denominators
  3. Understand a confidence interval and its assumptions
  4. Test how definitions and missingness change the answer
  5. Evaluate claims without chasing a significant result
  6. Deliver an uncertainty-aware evidence note
05Free · verified account

Visual analysis and decision-ready reporting

Turn checked analysis into clear charts, useful context and a reproducible report that supports a defensible next decision.

6 published lessons · 5h 30m suggested

You will produce
A report contract and chart specifications

View access requirements ↗
View the 6 lesson topics
  1. Define the audience and report contract
  2. Choose charts and scales that answer the question
  3. Show the context that changes the headline
  4. Build a readable, accessible report layout
  5. Write an evidence-led summary and recommendation
  6. Validate, reproduce and hand over the report
06Free · verified account

Applied analysis capstone

Bring the six-module method together in a reproducible service review: audit, analyze, challenge, report and explain your own contribution.

6 published lessons · 6h suggested

You will produce
A decision brief, plan and metric contract

View access requirements ↗
View the 6 lesson topics
  1. Turn the service brief into a capstone plan
  2. Audit the source and lock the metric contract
  3. Build and reconcile the complete analysis pipeline
  4. Challenge the explanation with subgroup and sensitivity checks
  5. Deliver a decision report and reproducible evidence pack
  6. Review the capstone and explain your own contribution

Only completed, published material is listed. All six Applied Data Analysis modules are prepared, ending with a guided capstone. Suggested times include practice and self-review; work at your own pace.

Ready to bring it together?

Show your method.
Explain your judgment.

The capstone connects source checks, calculations, challenge tests and a clear recommendation. Your final artifact is guided fictional practice, with your independent contribution and its limits made explicit.

Lesson completion is saved only for existing signed-in learners. Practice checks are not graded or saved, and local files stay on your device. No accreditation, employment outcome or operational approval is supplied.

Begin with the analysis plan →