AI & Data
AI & Data Scientist roadmap
From statistics and data wrangling to machine learning models and LLMs, ending with an applied capstone.
- Advanced
- about 23 hours
- 13 modules
- 29 steps
Before you start
What you will learn
1. Statistics Foundations
Summarize data with descriptive statistics, reason with probability and distributions, and spot sampling bias.
- Descriptive Statistics — Statistics · Descriptive Statistics
- Probability — Statistics · Probability
- Distributions — Statistics · Distributions
- Sampling & Bias — Statistics · Sampling & Bias
2. SQL FoundationsOptional
Query relational data with SELECT, filtering, sorting and joins.
- SQL Basics — Databases · SQL Basics
- Joins — Databases · Joins
3. Statistical Inference & Experiments
Draw conclusions from samples with hypothesis tests and confidence intervals, tell correlation from causation, run A/B tests and fit simple regressions.
- Hypothesis Testing — Statistics · Hypothesis Testing
- Confidence Intervals — Statistics · Confidence Intervals
- Correlation vs Causation — Statistics · Correlation vs Causation
- A/B Testing — Statistics · A/B Testing
- Regression Basics — Statistics · Regression Basics
4. Data Analysis with NumPy & pandas
Load, clean, join and aggregate tabular data with NumPy and pandas.
- NumPy — Data Analysis with Python · NumPy
- pandas Fundamentals — Data Analysis with Python · pandas Fundamentals
- Data Cleaning — Data Analysis with Python · Data Cleaning
- Merge & Join — Data Analysis with Python · Merge & Join
- GroupBy & Aggregation — Data Analysis with Python · GroupBy & Aggregation
5. Time Series & Reproducible Notebooks
Resample and window time-indexed data with pandas, and keep notebook analyses reproducible.
- Time Series — Data Analysis with Python · Time Series
- Notebooks & Reproducibility — Data Analysis with Python · Notebooks & Reproducibility
6. Data VisualizationOptional
Pick the right chart for the question, plot it with Matplotlib and Seaborn, and combine charts into clear dashboards.
- Choosing the Right Chart — Data Visualization & BI · Choosing the Right Chart
- Visualization with Matplotlib & Seaborn — Data Analysis with Python · Visualization with Matplotlib & Seaborn
- Dashboards — Data Visualization & BI · Dashboards
7. SQL for AnalyticsOptional
Summarize data with GROUP BY and HAVING, and answer harder questions with subqueries, CTEs and window functions.
- GROUP BY & HAVING — Databases · GROUP BY & HAVING
- Subqueries, CTEs & Window Functions — Databases · Subqueries, CTEs & Window Functions
8. ML Foundations
Supervised and unsupervised learning, training and validation, and the metrics used to judge models.
- Supervised & Unsupervised Learning — Machine Learning · Supervised & Unsupervised Learning
- Training, Validation & Cross Validation — Machine Learning · Training, Validation & Cross Validation
- Precision, Recall, F1 & ROC — Machine Learning · Precision, Recall, F1 & ROC
9. LLM Application Basics
How neural networks, transformers and LLMs work, tokens and context windows, and prompting for structured outputs.
- AI, Neural Networks & Transformers — AI Engineering · AI, Neural Networks & Transformers
- LLMs, Tokens & Context Windows — AI Engineering · LLMs, Tokens & Context Windows
- Prompt Engineering & Structured Outputs — AI Engineering · Prompt Engineering & Structured Outputs
10. Supervised Models
Regression and classification, the bias-variance trade-off and regularization, and tree ensembles.
- Regression & Classification — Machine Learning · Regression & Classification
- Bias, Variance & Regularization — Machine Learning · Bias, Variance & Regularization
- Decision Trees, Random Forest & Boosting — Machine Learning · Decision Trees, Random Forest & Boosting
11. Features, Clustering & Neural Networks
Engineer better features, find structure with clustering and PCA, and train neural networks.
- Feature Engineering — Machine Learning · Feature Engineering
- Clustering & PCA — Machine Learning · Clustering & PCA
- Neural Networks — Machine Learning · Neural Networks
12. RAG & RetrievalOptional
Embeddings and vector search, and retrieval-augmented generation with chunking and reranking.
- Embeddings & Vector Search — AI Engineering · Embeddings & Vector Search
- RAG, Chunking & Reranking — AI Engineering · RAG, Chunking & Reranking
13. AI & Data Scientist Capstone
A scenario-based final challenge mixing statistics, data wrangling, machine learning, model evaluation and LLMs.
- AI & Data Scientist Capstone — Capstones · AI & Data Scientist Capstone
Each step unlocks when you answer its questions well enough, and a module you finish counts in every roadmap that shares it.
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