You've completed all 18 modules of the Data Science Study Path. That's seriously impressive — you're a data scientist now.
A complete learning journey from Python basics to deep learning — interactive guides with real-world examples and hands-on practice.
Variables, strings, lists, dicts, loops, functions, classes, error handling, comprehensions, and modules.
Array creation, indexing, broadcasting, math operations, linear algebra, random number generation, and performance.
HTTP fundamentals, requests library, JSON parsing, authentication, pagination, rate limiting, async calls, FastAPI, and web scraping.
SELECT, WHERE, GROUP BY, JOINs, subqueries, CTEs, window functions, indexes, views, and SQLite with Python.
Git fundamentals to advanced — branching, merging, rebasing, remotes, pull requests, hooks, LFS, and GitHub Actions for data science teams.
The essential data analysis library — Series, DataFrames, cleaning, groupby, merge, datetime, string ops, and plotting.
Blazing-fast DataFrames — expressions, lazy API, groupby, joins, string/date ops, Parquet, and Pandas migration.
Descriptive stats, probability distributions, hypothesis testing, confidence intervals, ANOVA, and correlation.
Handle missing data, outliers, encoding, scaling, and create powerful features — the skill that improves models more than better algorithms.
Think probabilistically — Bayes' theorem, priors & posteriors, Bayesian A/B testing, Monte Carlo simulation, and MCMC for inference.
Line, bar, scatter, histogram, subplots, customization, heatmaps, twin axes, animations, and style sheets.
Statistical visualization — distributions, regression, heatmaps, pair plots, FacetGrid, and themes.
Interactive charts — scatter, bar, 3D, animations, geographic maps, subplots, funnel, and Sankey diagrams.
ML from scratch — regression, classification, clustering, pipelines, evaluation metrics, and hyperparameter tuning.
Pandas datetime indexing, resampling, rolling stats, ARIMA, seasonal decomposition, and forecasting evaluation.
Text cleaning, tokenization, NER, sentiment analysis, TF-IDF, topic modeling, text classification, and transformers.
PyTorch from tensors to production — autograd, CNNs, LSTMs, transfer learning, regularization, and model saving.
Build data web apps in pure Python — widgets, charts, forms, caching, file upload, session state, and multi-page apps.
Model saving, FastAPI serving, Docker containers, MLflow experiment tracking, monitoring, CI/CD, A/B testing, and explainability.