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Data scientist path

The Data scientist learning path chains 27 courses: 139 lessons, 411 exercises and about 83 hours of content. It starts with “Programming Basics and Algorithms” and ends with “Docker and Deployment”. This is the base order: your personal path is built from it and from your goal.

  • 27 courses
  • 139 lessons
  • 411 exercises
  • about 83 h
  • Runs in: browser, full project
  1. Programming Basics and Algorithms
  2. Terminal and Command Line
  3. Git and GitHub
  4. Coding with an AI Assistant (Prompts, Code Review)
  5. SQL Fundamentals (SELECT, Filters, Aggregates)
  6. Intermediate SQL (joins, subqueries)
  7. Advanced SQL (CTEs, window functions)
  8. Descriptive Statistics
  9. Probability and Inferential Statistics
  10. Python Fundamentals
  11. Intermediate Python (Functions, Modules, OOP)
  12. Data Analysis with pandas and NumPy
  13. Data Cleaning and Preparation
  14. Visualization (Matplotlib, Seaborn, Plotly)
  15. Exploratory Data Analysis
  16. Introduction to Machine Learning (scikit-learn)
  17. Supervised Learning: Regression and Classification
  18. Unsupervised Learning: Clustering and Dimensionality Reduction
  19. Model Evaluation and Validation
  20. Feature Engineering
  21. Time Series and Forecasting
  22. HTTP and REST APIs
  23. APIs with FastAPI
  24. Deep Learning with PyTorch
  25. Natural Language Processing (NLP) and LLMs
  26. Deploying a Model (API, MLOps Basics)
  27. Docker and Deployment
  • Foundations
  • SQL
  • Statistics
  • Python
  • Data analysis
  • Data science
  • Back-end
  • Python for the web
Build my pathBecome a data scientist

The 27 courses, in order

  1. Foundations4 courses
    1. 01Programming Basics and AlgorithmsLearn to program step by step in Python: variables, conditions, loops, functions and your first algorithms.Beginner5 lessons2 h 25 minBrowser
    2. 02Terminal and Command LineMaster the terminal: navigation, file management, searching, pipes, bash scripts and permissions.Beginner5 lessons2 h 20 minFull project
    3. 03Git and GitHubVersion your code with Git and collaborate on GitHub: commits, branches, conflicts, push, pull requests.Beginner5 lessons3 h 5 minFull project
    4. 04Coding with an AI Assistant (Prompts, Code Review)Use an AI assistant to code effectively: precise prompts, critical review, tests, debugging and security.Beginner5 lessons2 h 30 minBrowser
  2. SQL3 courses
    1. 05SQL Fundamentals (SELECT, Filters, Aggregates)Learn to query a database in SQL: select, filter, sort, count and summarize data by groups.Beginner5 lessons2 h 9 minBrowser
    2. 06Intermediate SQL (joins, subqueries)Combine several tables with joins, write subqueries, and master CASE WHEN, UNION and EXCEPT.Intermediate5 lessons2 h 32 minBrowser
    3. 07Advanced SQL (CTEs, window functions)Write analytical queries with CTEs, recursive CTEs and window functions: rankings, running totals, period-over-period changes.Advanced5 lessons2 h 47 minBrowser
  3. Statistics2 courses
    1. 08Descriptive StatisticsSummarize and describe data: variable types, mean, median, spread, quantiles, outliers and correlation, using Python.Beginner5 lessons2 h 26 minBrowser
    2. 09Probability and Inferential StatisticsGo from sample to population: probability, distributions, the central limit theorem, confidence intervals and hypothesis tests in Python.Intermediate5 lessons2 h 34 minBrowser
  4. Python5 courses
    1. 10Python FundamentalsLearn the basics of Python: variables, conditions, loops, lists, dictionaries, functions and error handling.Beginner5 lessons2 h 30 minBrowser
    2. 11Intermediate Python (Functions, Modules, OOP)Write structured Python: advanced functions, generators, modules, classes, inheritance, dataclasses and custom exceptions.Intermediate5 lessons3 hBrowser
    3. 12Data Analysis with pandas and NumPyAnalyze data in Python: NumPy arrays, pandas DataFrames, filters, computed columns, groupby, pivot tables and joins.Intermediate6 lessons3 h 20 minBrowser
    4. 13Data Cleaning and PreparationClean real-world data with pandas: diagnosis, missing values, types, duplicates, outliers and a reproducible pipeline.Intermediate5 lessons3 h 5 minBrowser
    5. 14Visualization (Matplotlib, Seaborn, Plotly)Build clear charts in Python with Matplotlib, Seaborn and Plotly: choosing the right visual, formatting, facets and interactivity.Intermediate5 lessons3 h 5 minBrowser
  5. Data analysis1 course
    1. 15Exploratory Data AnalysisExplore a dataset with pandas: profile, missing values, duplicates, distributions, relationships, anomalies, time and summary of findings.Intermediate5 lessons3 h 1 minBrowser
  6. Data science6 courses
    1. 16Introduction to Machine Learning (scikit-learn)Discover machine learning with scikit-learn: vocabulary, fit/predict, train/test, preprocessing, and pipelines.Intermediate5 lessons2 h 20 minBrowser
    2. 17Supervised Learning: Regression and ClassificationMaster the main supervised models with scikit-learn: linear, regularized, logistic, trees, kNN, and ensembles.Intermediate5 lessons2 h 50 minBrowser
    3. 18Unsupervised Learning: Clustering and Dimensionality ReductionSegment and summarize your data without labels: K-means, DBSCAN, hierarchical clustering, PCA, and t-SNE with scikit-learn.Intermediate5 lessons2 h 50 minBrowser
    4. 19Model Evaluation and ValidationEvaluate your models rigorously: suitable metrics, ROC and thresholds, cross-validation, hyperparameter tuning, leakage.Intermediate5 lessons2 h 55 minBrowser
    5. 20Feature EngineeringPrepare high-performing variables: transformations, encodings, missing values, ColumnTransformer, dates, and selection.Advanced6 lessons3 h 35 minBrowser
    6. 21Time Series and ForecastingAnalyze and forecast time series: pandas, decomposition, baselines, exponential smoothing, ARIMA, and ML models.Advanced5 lessons3 h 28 minBrowser
  7. Back-end1 course
    1. 22HTTP and REST APIsUnderstand HTTP and design clean REST APIs: methods, status codes, JSON, errors, caching, CORS, authentication, and routing.Intermediate6 lessons3 h 50 minBrowser
  8. Python for the web1 course
    1. 23APIs with FastAPIBuild REST APIs in Python with FastAPI: routes, Pydantic validation, CRUD, dependencies, database and tests.Intermediate5 lessons3 h 33 minFull project
  9. Data science3 courses
    1. 24Deep Learning with PyTorchMaster the basics of deep learning with PyTorch: tensors, autograd, networks, training, regularization, CNNs, and transfer learning.Advanced5 lessons3 h 58 minFull project
    2. 25Natural Language Processing (NLP) and LLMsFrom TF-IDF to LLMs: tokenization, embeddings, Transformers, RAG and LoRA fine-tuning, with rigorous evaluation.Advanced6 lessons4 h 21 minFull project
    3. 26Deploying a Model (API, MLOps Basics)Put a model into production: serialization, FastAPI API, Docker, tests, CI, MLflow registry and drift monitoring.Advanced5 lessons4 h 3 minFull project
  10. Back-end1 course
    1. 27Docker and DeploymentContainerize your applications with Docker, orchestrate them with Compose, and deploy them to production with CI/CD and HTTPS.Advanced5 lessons3 h 59 minFull project

Optional courses

Suggested depending on your goal, on top of the base path.

Other paths

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    3. SQL Fundamentals (SELECT, Filters, Aggregates)
    4. Intermediate SQL (joins, subqueries)
    5. Descriptive Statistics
    6. Python Fundamentals
    7. Data Analysis with pandas and NumPy
    8. Data Cleaning and Preparation
    9. Visualization (Matplotlib, Seaborn, Plotly)
    10. Probability and Inferential Statistics
    11. Exploratory Data Analysis
    12. Business Metrics and KPIs
    13. Advanced SQL (CTEs, window functions)
    14. Data Storytelling and Presenting Results
    15. Automate with Python (Files, Excel, APIs)
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Say what you want to be able to do, we build your path

Your path is assembled from the catalogue courses. With the Pro plan, every lesson is told with examples from your job.

The preview is free, and you can take the courses one at a time without paying. The path only contains lessons from the catalogue.