Developer learning domain

Data Science & AI Learning Path & Roadmap

Move from Python and statistics into data analysis, visualization, machine learning, deep learning, AI engineering, and reliable data pipelines.

See the learning order Browse 7 paths

Dependency-aware sequence

Recommended Learning Order

Start with foundations, then move through the paths in an order that matches their prerequisite level.

1
Data Analysis with Python
Clean, explore, summarize, validate, combine, and communicate findings from structured datasets through reproducible Python workflows.
Open path
2
Statistics for Data Science
Summarize data, reason about probability and sampling, interpret confidence intervals and tests, and communicate statistical uncertainty responsibly.
Open path
3
Data Visualization
Select, create, evaluate, and communicate clear, accessible visualizations for distributions, comparisons, relationships, time series, and dashboards.
Open path
4
Machine Learning Fundamentals
Prepare data, train baseline regression and classification models, evaluate predictions, detect leakage and overfitting, and document limitations.
Open path
5
Deep Learning Fundamentals
Build, train, evaluate, diagnose, and document neural networks while understanding optimization, representation, reproducibility, and deployment constraints.
Open path
6
AI Engineering
Build reliable AI-powered applications with model interfaces, structured outputs, retrieval, evaluation, safety controls, privacy, observability, and cost management.
Open path
7
Data Engineering Fundamentals
Design, build, test, secure, observe, and operate reliable batch and streaming-oriented data pipelines, storage layers, models, and workflows.
Open path

Structured curriculum

Learning Paths in This Domain

Practitioner · 40 hours

Data Analysis with Python

Clean, explore, summarize, validate, combine, and communicate findings from structured datasets through reproducible Python workflows.

View path

Foundation · 30 hours

Statistics for Data Science

Summarize data, reason about probability and sampling, interpret confidence intervals and tests, and communicate statistical uncertainty responsibly.

View path

Practitioner · 40 hours

Data Visualization

Select, create, evaluate, and communicate clear, accessible visualizations for distributions, comparisons, relationships, time series, and dashboards.

View path

Practitioner · 40 hours

Machine Learning Fundamentals

Prepare data, train baseline regression and classification models, evaluate predictions, detect leakage and overfitting, and document limitations.

View path

Professional · 50 hours

Deep Learning Fundamentals

Build, train, evaluate, diagnose, and document neural networks while understanding optimization, representation, reproducibility, and deployment constraints.

View path

Professional · 50 hours

AI Engineering

Build reliable AI-powered applications with model interfaces, structured outputs, retrieval, evaluation, safety controls, privacy, observability, and cost management.

View path

Professional · 50 hours

Data Engineering Fundamentals

Design, build, test, secure, observe, and operate reliable batch and streaming-oriented data pipelines, storage layers, models, and workflows.

View path

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