Prepare datasets and evaluate basic models under supervision.
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Analyze a resumeFoundation → intermediate · 280–560 focused hours of overlapping study and project practice. Job-readiness development is ongoing.
Editorial planning ranges based on curriculum scope—not measured universal learning times. Prior knowledge and practice quality change the journey.
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Suggested next step: Python →Python is a general-purpose programming language.
60–120 focused hours · roughly 6.0–12.0 weeks at your schedule · Beginner
Prerequisites: No specific prerequisite.
Milestone: Build a CSV reporting command-line tool.
NumPy provides efficient numerical arrays.
20–40 focused hours · roughly 2.0–4.0 weeks at your schedule · Beginner
Prerequisites: Python
Milestone: Implement vectorized dataset summaries.
Pandas manipulates tabular data in Python.
25–50 focused hours · roughly 2.5–5.0 weeks at your schedule · Beginner
Prerequisites: Python
Milestone: Clean a messy sales dataset with an audit report.
Statistics helps reason about data and uncertainty.
60–120 focused hours · roughly 6.0–12.0 weeks at your schedule · Beginner
Prerequisites: No specific prerequisite.
Milestone: Compare two groups and explain uncertainty and confounders.
ML learns patterns from examples rather than fixed rules.
100–200 focused hours · roughly 10.0–20.0 weeks at your schedule · Intermediate
Prerequisites: Python · NumPy · Statistics
Milestone: Compare a baseline and classifier on held-out data.
Scikit-learn provides standard ML algorithms and evaluation tools.
30–60 focused hours · roughly 3.0–6.0 weeks at your schedule · Intermediate
Prerequisites: Machine Learning · Pandas
Milestone: Build a cross-validated tabular classification pipeline.
Model evaluation checks performance and failure modes.
30–60 focused hours · roughly 3.0–6.0 weeks at your schedule · Intermediate
Prerequisites: Machine Learning
Milestone: Write an error-analysis report with limitations.
Git records changes to source code.
8–16 focused hours · roughly 0.8–1.6 weeks at your schedule · Beginner
Prerequisites: No specific prerequisite.
Milestone: Track a small project and resolve a practice merge conflict.
Clean data and document missing values without target leakage.
Skills practiced: Pandas · NumPy
Compare a simple baseline and classifier on a held-out split.
Skills practiced: Scikit-learn · Machine Learning
Run cross-validation, analyze errors and write limitations.
Skills practiced: Model Evaluation · Statistics · Git
Career recommendations and learning timelines are educational guidance based on your resume and selected goals. They are not guarantees of employment or professional competency. Timelines vary; external resources can change. Skill guides link the source curricula used to plan this path.