Explore data, design experiments and communicate defensible model-based insights.
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Analyze a resumeFoundation → advanced · 500–1000 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.
SQL queries and manages relational data.
25–60 focused hours · roughly 2.5–6.0 weeks at your schedule · Beginner
Prerequisites: No specific prerequisite.
Milestone: Answer ten business questions against a sample database.
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.
Visualization communicates data with charts.
25–50 focused hours · roughly 2.5–5.0 weeks at your schedule · Beginner
Prerequisites: No specific prerequisite.
Milestone: Create an accessible dashboard with three explained charts.
Experimentation tests decisions with controlled comparisons.
40–80 focused hours · roughly 4.0–8.0 weeks at your schedule · Intermediate
Prerequisites: Statistics · SQL
Milestone: Design an A/B experiment and state its assumptions.
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.
Feature engineering prepares useful model inputs.
30–60 focused hours · roughly 3.0–6.0 weeks at your schedule · Intermediate
Prerequisites: Pandas · Machine Learning
Milestone: Compare features using cross-validation and document leakage checks.
Communication shares information clearly and responsibly.
15–35 focused hours · roughly 1.5–3.5 weeks at your schedule · Beginner
Prerequisites: No specific prerequisite.
Milestone: Present a dashboard recommendation and its limitations.
An API exposes data and operations through defined requests.
20–45 focused hours · roughly 2.0–4.5 weeks at your schedule · Beginner
Prerequisites: No specific prerequisite.
Milestone: Design an inventory CRUD API with failure cases.
Docker packages applications into containers.
25–50 focused hours · roughly 2.5–5.0 weeks at your schedule · Beginner
Prerequisites: Linux
Milestone: Containerize a small API with a persistent database.
Model serving exposes trained models for prediction.
40–80 focused hours · roughly 4.0–8.0 weeks at your schedule · Intermediate
Prerequisites: Machine Learning · REST API · Docker
Milestone: Serve a model with input validation and latency tests.
Analyze a public dataset with uncertainty and quality checks.
Skills practiced: Pandas · Statistics
State a hypothesis, confounders and an analysis plan.
Skills practiced: Experimentation · SQL
Evaluate a pipeline, document errors and demonstrate inference.
Skills practiced: Scikit-learn · Model Evaluation · Model Serving
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.