Career studio / Machine Learning
Build evidence for the Junior Data Scientist role
A fictional learning platform wants to understand course completion patterns. Build a small modeling plan that avoids using information available only after the outcome.
Your outcome: Exploratory analysis, leakage-aware baseline and model evaluation report.
Start with the right expectations
Who this is for
Python data handling, descriptive statistics and basic algebra.
This path develops a demonstrable work sample for the named role. It does not replace employer-specific experience, professional qualifications or supervised practice.
Plan your study time
Use a flexible six-week project schedule: one week for each of the first four stages and two weeks for the portfolio and review. Allow roughly 3–5 hours per week for the case work, in addition to course study. This is a planning suggestion, not a measured course duration or a promise of job readiness.
Read the course access notes before starting. Free viewing or reading does not automatically include certificates, graded assessments, software licences or paid cloud resources. Core case work can be documented with a free editor and spreadsheet; optional implementation tools may have additional requirements.
Your course sequence
Start in the order below. Use the course outline to select the sections relevant to the stage tasks, and complete foundation exercises before moving on. Full provider courses can be substantially longer than this project schedule. These are course resources, not a claimed count of individual lessons.
Resource 01 / Google
Free Advanced Data Analytics Course: Statistics and ML
Purpose: Build the foundations. Connect your notes to the case tasks below rather than only collecting completion marks.
Access: Free Video
Certificate: Free video viewing; Google certification is not included
Resource 02 / Google
Machine Learning Crash Course
Purpose: Develop the role-specific method. Connect your notes to the case tasks below rather than only collecting completion marks.
- Understand prediction models: explore regression, loss, gradient descent and classification.
- Evaluate classification results: consider thresholds and measures such as precision and recall.
- Prepare machine learning data: work through numerical and categorical data concepts.
Access: Public learning materials are free. Programming exercises use free Colab with a Google account; free computing resources have usage and availability limits.
Certificate: Free module badges after at least 80% on the relevant end-of-module quiz. Google does not offer formal certification for this course.
Resource 03 / Harvard
CS 50 Introduction to Programming with Python
Purpose: Strengthen practical application. Connect your notes to the case tasks below rather than only collecting completion marks.
- Write functions, work with variables and use conditions and loops to control a program.
- Handle exceptions and debug behavior that differs from your expectations.
- Use libraries and write unit tests for reusable functions.
Access: Free CS 50 OpenCourseWare material and free development tools; free edX and GitHub accounts for assignment submission and free-certificate workflow. No required paid hosting or software.
Certificate: Free CS 50 certificate after at least 70% on each required problem and the final project. No academic credit.
Learn, make, check
Keep a decision log as you work. For each stage, capture the assumption, your work product, a check and the change you made after that check. The acceptance criterion is the stopping condition for the stage.
Stage 1 / suggested project week 1
Frame the decision
Understand: A prediction target must support a defined action.
Make: Define a completion target, observation window and intended support action.
Check before moving on: Explain the cost of false positives and false negatives.
Study companion: Free Advanced Data Analytics Course: Statistics and ML. Use its relevant lessons and examples; the independent WikiFree task is not a provider assessment.
Save as evidence: a dated work product and a short note explaining one choice, one alternative and one remaining uncertainty.
Stage 2 / suggested project week 2
Inspect the data
Understand: Associations do not establish causes.
Make: Create a data dictionary and mark features available before the prediction time.
Check before moving on: Exclude outcome-derived fields such as final certificate status.
Study companion: Machine Learning Crash Course. Use its relevant lessons and examples; the independent WikiFree task is not a provider assessment.
Save as evidence: a dated work product and a short note explaining one choice, one alternative and one remaining uncertainty.
Stage 3 / suggested project week 3
Build a baseline
Understand: A complex model must earn its complexity.
Make: Specify a simple majority-class baseline and a train/validation/test strategy.
Check before moving on: Do not tune decisions using the final test set.
Study companion: CS 50 Introduction to Programming with Python. Use its relevant lessons and examples; the independent WikiFree task is not a provider assessment.
Save as evidence: a dated work product and a short note explaining one choice, one alternative and one remaining uncertainty.
Stage 4 / suggested project week 4
Evaluate responsibly
Understand: Overall accuracy can hide class imbalance and subgroup errors.
Make: Calculate precision and recall from the sample confusion counts and discuss error review.
Check before moving on: State that a tiny synthetic fixture cannot establish real-world performance.
Study companion: CS 50 Introduction to Programming with Python. Use its relevant lessons and examples; the independent WikiFree task is not a provider assessment.
Save as evidence: a dated work product and a short note explaining one choice, one alternative and one remaining uncertainty.
Stage 5 / suggested project weeks 5–6
Present your portfolio
Assemble the four work products into Exploratory analysis, leakage-aware baseline and model evaluation report. Write a README explaining the problem, your assumptions, the decisions you made, tests actually performed and what remains incomplete.
Give a five-minute walkthrough: one minute for the problem, two for the work and evidence, one for trade-offs and one for the next improvement. If you have not implemented a proposed system, label it a design or prototype rather than a deployed solution.
Acceptance: a reviewer can locate each work product, reproduce the key reasoning and identify the limits without a private explanation from you.
The portfolio brief
A fictional learning platform wants to understand course completion patterns. Build a small modeling plan that avoids using information available only after the outcome.
Deliver: Exploratory analysis, leakage-aware baseline and model evaluation report.
| confusion cell | count |
|---|---|
| True positive | 30 |
| False positive | 10 |
| False negative | 20 |
| True negative | 40 |
Use these small inputs to make the reasoning visible. They are not a representative dataset, a real client engagement or evidence of production performance. Where the brief requires additional sample records, create them yourself, label them synthetic and explain why you added them.
The ZIP includes the case CSV, four stage worksheets, course sequence, decision log, test log, portfolio README template, review rubric and reference reasoning. It is a starter kit for your work, not a completed application, professional report or proprietary project file.
Check your reasoning
With TP 30, FP 10 and FN 20, what are precision and recall?
Write your answer and assumptions before opening the reference.
Read the reference reasoning
Precision=30/(30+10)=75%; recall=30/(30+20)=60%. Report the target class and evaluation population with these values.
Portfolio review rubric
| Criterion | Points | Evidence required |
|---|---|---|
| Problem and scope | 4 | A clear user, need, boundary and assumptions |
| Role-specific work | 8 | 2 points for each of the four stage acceptance checks |
| Reasoning and validation | 4 | Independent checks, actual results and uncertainty |
| Communication and reproducibility | 4 | A usable README, source links and reviewable work products |
Aim for at least 16/20 as a self-review target, with no missing stage acceptance check. This is a WikiFree practice rubric, not an industry credential or a hiring assessment.
Interview practice
Which feature would cause leakage, and why was it unavailable at prediction time?
Answer using the case: context, your action, evidence and limitation. Describe this honestly as a self-directed project. Do not present fictional organizations, invented interviews or practice results as paid work experience.
Your progress
Use this checklist as a personal study aid. Progress is stored in this browser when local storage is available; it is not an account record or a certificate.
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Questions before you start
Will this path qualify me for a job?
It helps you study relevant foundations and produce a work sample. Employers may require additional skills, qualifications and experience. Review actual role requirements before applying; no job or certification is guaranteed.
Are all tools and certificates free?
The linked learning routes were selected for free viewing or reading. Follow the separate access and certificate notes on each course. Paid tools and cloud deployment are optional; document a local design when implementation resources are unavailable.
Can I follow this on a phone?
You can read the path and many learning resources on a phone. Coding, detailed spreadsheet work, design tools and mobile-app development are easier or require a suitable computer. The page is designed to reflow; that is not a claim that every physical device has been tested.
Questions & discussion
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