Assess your readiness for machine learning after analytics

Readiness is not a count of completed videos. Machine learning builds on data inspection, row grain, probability, validation and communication. A short diagnostic can identify what to practise before model complexity hides the gap.

Use evidence by area

The career evidence lab scores an invented learner record from 0 to 3 in five areas.

from career_cases import readiness_case

result = readiness_case()
assert result["score"] == 12
assert result["gaps"] == ["validation"]
assert result["recommended_start"] == "validation foundations"
assert result["placement_claim"] is False

The total is less useful than the pattern. This fixture has strong Python/SQL/communication evidence and adequate statistics, but validation scores 1. The recommendation is to repair that boundary before advanced modelling. This is an authored study aid, not a standardized or placement assessment.

Try five small tasks

1. 1Python data work: load an unfamiliar table, assert its grain, handle missing values and write a reusable transformation.
2. 2SQL and grain: join orders to items without double-counting order revenue; reconcile the result independently.
3. 3Statistics: explain conditional probability, sampling variation and a confidence interval using a practical example.
4. 4Validation: choose a split for repeated customers over time and prevent preprocessing leakage.
5. 5Communication: write a one-page decision memo with metric definition, denominator and limitation.

Score each task from 0 (cannot start) to 3 (correct, reproducible and explainable). Keep artifacts and ask someone to challenge your reasoning.

Route the gap into practice

A low validation score calls for split diagrams, train-only pipelines and leakage injections. A low statistics score calls for simulations and interpretation, not formula copying. Weak SQL grain needs reconciliation exercises before building features. Reassess with a new dataset so memorization cannot masquerade as transfer.

You do not need perfection before starting modelling. You need enough foundation to notice when a model result is invalid and a plan to close specific gaps.

The Data Science course can use this diagnostic to target the analyst foundation within the existing programme. Current eligibility and delivery details must come from the official course information.

Exercise

Complete all five tasks in 90 minutes without a tutorial. Record mistakes, create a two-week repair plan for the two weakest areas and repeat with a fresh dataset. Compare evidence, not confidence ratings alone.

Continue learning

This article is part of the Data science careers and portfolio decisions sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the prerequisite or neighbouring task in Choose a data science course by its evaluation and deployment work.
• Continue with Build a statistics revision routine from practical mistakes.

Reference: scikit-learn common pitfalls.