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Become a data scientist

The catalogue prepares you for the data scientist role with a path of 27 courses (139 lessons, 411 exercises, about 83 hours), from “Programming Basics and Algorithms” to “Docker and Deployment”. 34 catalogue courses list this job among their outcomes.

  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
Build my pathSee the 27 courses in order

The steps of the path

  1. Foundations

  2. SQL

  3. Statistics

  4. Python

  5. Data analysis

  6. Data science

  7. Back-end

  8. Python for the web

  9. Data science

  10. Back-end

Skills practised

Foundations29

VariablesBasic typesif / elif / else conditionsfor and while loopsListsFunctionsAlgorithmicsNavigationFile managementSearching with grepPipesBash scriptsPermissionsEnvironment variables

15 more skills

CommitStaging areaHistory (log, diff)BranchesMergeConflictsRemote repositoryPull requestLanguage model (LLM)Prompt writingReviewing generated codeAutomated testsDebuggingSecurity of generated codeResponsible use

SQL25

SELECT and FROMWHERE clauseORDER BYDISTINCTCOUNT, SUM, AVG, MIN, MAXGROUP BYHAVINGTesting NULL with IS NULLINNER JOINLEFT JOINAnti-joinSelf-joinSous requetesExists

11 more skills

CASE WHENUNION and UNION ALLCTE (WITH)WITH RECURSIVEOVER clausePARTITION BYROW_NUMBER, RANK, DENSE_RANKRunning totalROWS BETWEEN frameLAG and LEADNTILE

Statistics19

Variable typesCounts and frequenciesMeanMedianVarianceStandard deviationQuantilesOutliersPearson correlationPandasProbabilityBayes' theoremNormal distributionBinomial distribution

5 more skills

Central limit theoremConfidence intervalHypothesis testp-valueScipy

Python79

VariablesBasic typesf-stringsConditionsBooleans and logical operatorsListsfor and while loopsList comprehensionsDictionariesTuplesSetsFunctionstry/except exceptionsAdvanced parameters

65 more skills

Type annotationsLambda functionsHigher-order functionsGeneratorsModules and importsStandard libraryVirtual environments and pipClasses and instancesMethodsSpecial methodsInheritanceDataclassesCustom exceptionsndarray arraysVectorized computationBoolean masksSeriesDataFrameReading CSV filesInitial explorationloc and ilocBoolean filteringSortingComputed columnsRecoding valuesDates with .dtText with .strgroupbyMultiple aggregationsPivot tablesCountsConcatenationJoinsJoin validationQuality diagnosisDetecting missing valuesDetecting duplicatesMissing valuesImputationImputation by groupType conversionto_numericto_datetimecategory typeText standardizationRemoving duplicatesOutliersmelt and pivotCleaning pipelineData validationFigure and AxesLinesTitles, labels and legendChoosing a chartBars and histogramsScatter plots and box plotsSubplotsAxis formattingAnnotationsAxes-level functionsColor encoding (hue)FacetsPlotly ExpressFigure structureHTML export

Data analysis10

Data profilingMissing valuesDuplicatesUnivariate analysisBivariate analysisSimpson's paradoxChange over timeAnalysis summaryPandasMatplotlib

Data science74

Machine learningFeatures and target variableEstimatorTrain/test splitOverfittingStandardizationPipelineCross-validationLinear regressionMAE, RMSE, R²RegularizationLogistic regressionDecision treek-nearest neighbors

60 more skills

Random forestGradient boostingClusteringK-meansSilhouette scoreDBSCANHierarchical clusteringPrincipal component analysist-SNESegmentationConfusion matrixPrecision and recallROC curve and AUCDecision thresholdHyperparameter tuningBias-variance trade-offData leakageNumerical transformationsOne-hot encodingTarget encodingImputationColumnTransformerTime-based featuresCyclical encodingFilter-based selectionPermutation importancePandasDecompositionStationarityEvaluationExponential smoothingARIMA / SARIMALag and calendar featuresBacktestingTensorAutogradnn.ModuleTraining loopRegularisationConvolutional networkTransfer learningBasesTokenizationTF-IDFText classificationEmbeddingTransformer architectureLarge language modelRetrieval-Augmented GenerationLoRA / PEFTLLM evaluationSerializationInference APIFastAPI and PydanticDockerModel testsContinuous integration and deploymentModel registryMonitoringData drift

Back-end20

HTTP request and responseURL structureHTTP methodsStatus codesREST principlesPaginationData validationStructured errors (Problem Details)HTTP cache and ETagCORSRoutingContainersImagesDockerfile

6 more skills

Multi-stage buildImage securityDocker ComposeImage registryCI/CDReverse proxy and HTTPS

Python for the web8

Path operations (routes)Automatic validationPydantic modelsCRUD operationsDependency injectionDatabaseTests with TestClientREST principles

Courses that mention this job

Frequently asked questions

How long does the Data scientist path take?

The base path is about 83 hours of lessons and exercises, across 27 courses and 139 lessons. You go at your own pace, lesson by lesson.

Do I need any prior knowledge to start?

No. The path starts with “Programming Basics and Algorithms”, a course with no prerequisites.

Which domains does the path cover?

In order: Foundations, SQL, Statistics, Python, Data analysis, Data science, Back-end, Python for the web. The last course is “Docker and Deployment”.

Does the path lead to a certification?

Not at this stage. The platform does not issue certifications yet, and claims no RNCP registration, no Qualiopi certification and no CPF eligibility.

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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 (opening after the beta), 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.