BOOTCAMP

Change your career with our Data Science course

Explore the fundamentals of data science and land your dream job with the #1 ranked bootcamp in the world.

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⭐️

4,98/5 - 5,200+ student reviews

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Course info

Master the fundamental skills of a Data Scientist
in just a few weeks

✔

Data Analytics Toolkits

Master the fundamental tools of programmatic data-analysts: Python as a backbone, SQL for queries and Jupyter Notebooks for visualization.

  • Python
  • SQL
  • Jupyter
  • Matplotlib
✔

Decision Science

Leverage statistics to build advanced analyses and take well-informed business decisions: statistical inferences, hypothesis testing, multivariate regression and confidence intervals.

  • PandasPandas
  • NumpyNumpy
  • StatsmodelsStatsmodels
✔

Machine Learning & Deep Learning

Master Machine Learning and Deep Learning workflows (data preparation, model selection, evaluation, and fine-tuning) and understand mathematical concepts and numerical implementations behind models.

  • Scikit-LearnScikit-Learn
  • TensorflowTensorflow
  • Google ComputeGoogle Compute
✔

ML Engineering & Team Projects

Package your models into replicable Python code that can be trained on big data in the cloud, using virtual machines and online databases. Monitor & retrain models when needed, and expose them to the world through APIs.

  • GitGit
  • DockerDocker
  • MLflowMLflow
  • FastAPIFastAPI

You can start for FREE! Join a live workshop for web development, data analytics, or UX design.

What our Data Science bootcamp looks like

Check out our curriculum week after week.

Prepwork: get ready to start the bootcamp!

Once you’ve been accepted, you'll receive about 40 hours of online learning resources, carefully curated by Le Wagon to be intuitive and interactive. This prepwork will ensure you have the necessary foundations in Python and maths before the bootcamp starts.

  • Setup your learning environment (notes, text editor, ...)
  • Terminal, Git and OS basic commands
  • Python foundations 
  • Maths foundations (in a fun & intuitive way!)

What you will do in practice

  • 40 hours of online tutorials
  • Curated resources from Le Wagon to gain solid foundations

Data Analysis

Learn Python for data science: extract data from relational databases, manipulate big data matrix and build visualizations. Understand key maths concepts for data analysis like statistics & linear algebra.

  • Source data from files, web-scraping, or APIs
  • Manipulate data with Python, Pandas & Numpy
  • Query / store data with SQL & Google Big Query
  • Visualization with Jupyter Notebook, Matplotlib, Seaborn & Plotly

What you will build in practice

  • A database built by scraping data from online bookstores
  • Advanced analysis of football performances in SQL
  • Visual dashboard connected to stock-market APIs

Decision Science

Put yourself in the shoes of a data consultant, and learn how to survive the data preparation phase of a vast dataset. Extract insights by interpreting statistical results based on multivariate regression models, hypothesis testing, and confidence intervals.

  1. Statsmodels for multivariate linear/logistic regressions
  2. Packaging and object-oriented programming in Python
  3. Notebook-based presentations with interactive graphs

What you will build in practice

  • 40h-long data consulting challenge based on real data from a marketplace
  • Individual presentations of your key findings to your client

Machine Learning

Implement the Machine Learning workflow with Scikit-Learn (data preparation, feature engineering, model selection, evaluation, and fine-tuning) and understand maths intuitions and numerical implementations of ML models.

  • Scikit-learn and XGBoost libraries
  • Supervised learning (linear, KNN, SVM, Trees, Ensembles)
  • Unsupervised learning (PCA, K-means, t-SNE, DBSCAN)
  • Structured data (tabular, time-series with SARIMAX...)
  • Unstructured data (images, text with Naive-Bayes, Tf-idf, LDA...)

What you will build in practice

  • Machine Learning models perfectly fine-tuned to your tasks
  • Pipelines combining data processing and model predictions
  • Image compression model by color clustering 
  • Spam detection algorithms
  • Prediction model for house prices

Deep Learning

Unveil the magic behind Deep Learning! Understand the architecture of neural networks (neurons, layers, stacks) and their parameters (activations, losses, optimisers). Build your own neural networks (dense, recurrent, or convolutional), to work on images, sequences and texts. Learn how to re-use and transfer learning from pre-trained “big-models”  from latest open-source research! Get your hands dirty with auto-encoders, batch data processing pipelines, and GPU training.

  • Scikit-learn and XGBoost libraries
  • Supervised learning (linear, KNN, SVM, Trees, Ensembles)
  • Unsupervised learning (PCA, K-means, t-SNE, DBSCAN)
  • Structured data (tabular, time-series with SARIMAX...)
  • Unstructured data (images, text with Naive-Bayes, Tf-idf, LDA...)

What you will build in practice

  • Dense neural network for fraud transaction detection
  • Transfer learning for image classification
  • Auto-encoders for image compression and denoising
  • Recurrent networks for weather forecasting
  • World embedding for sentiment analysis or text auto-completion

Machine Learning Engineering (MLOps)

Turn your best handcrafted models into a replicable python package that can be trained on big data in the cloud, using virtual machines and online databases. Monitor your model performance as new data comes in, retrain it when needed, and expose its predictions to the world via APIs or websites.

  • VS code & command line
  • Google Cloud, Virtual Machines, SSH for the trainer
  • MLflow & Prefect for DAG orchestration
  • Docker & Fast API for the backend
  • Streamlit for the frontend

What you will build in practice

  • ML model for predicting taxi fares, trained on big data on the cloud with GPUs
  • Visual web dashboard showing live predictions (on charts, maps, etc.)
  • Trained models in production capable of self-healing

Project weeks

Projects Phase is the ultimate experience of the course. Collaborate efficiently in teams of 3-4 people on a real data science project that you will either join or pitch to your class. Use either open-data repositories (government initiatives, Kaggle, Paper with Code, etc...) or bring your own private dataset. With full-time mentoring from expert teachers, let your wildest dream come true!

What you will build in practice

  • Make an app with a live demo of model predictions
  • Create an in-depth analysis of a business dataset
  • Replicate latest AI research papers with Big Models!

Career Week: start your career in data science!

Meet data science experts working in startups or large companies, prepare your CV and do mock interviews to prepare your job search. Go deeper into essential data topics.

  • Preparation for your job search
  • Connection to our 15,000 alumni and 985+ hiring partners

What you will do in practice

  • 1:1 coaching 
  • Review of your CV and cover letter
  • Preparation for technical interviews

Prepwork: get ready to start the bootcamp!

Once you’ve been accepted, you'll receive about 40 hours of online learning resources, carefully curated by Le Wagon to be intuitive and interactive. This prepwork will ensure you have the necessary foundations in Python and maths before the bootcamp starts.

  • Setup your learning environment (notes, text editor, ...)
  • Terminal, Git and OS basic commands
  • Python foundations 
  • Maths foundations (in a fun & intuitive way!)

What you will do in practice

  • 40 hours of online tutorials
  • Curated resources from Le Wagon to gain solid foundations

Data Analysis

Learn Python for data science: extract data from relational databases, manipulate big data matrix and build visualizations. Understand key maths concepts for data analysis like statistics & linear algebra.

  • Source data from files, web-scraping, or APIs
  • Manipulate data with Python, Pandas & Numpy
  • Query / store data with SQL & Google Big Query
  • Visualization with Jupyter Notebook, Matplotlib, Seaborn & Plotly

What you will build in practice

  • A database built by scraping data from online bookstores
  • Advanced analysis of football performances in SQL
  • Visual dashboard connected to stock-market APIs

Decision Science

Put yourself in the shoes of a data consultant, and learn how to survive the data preparation phase of a vast dataset. Extract insights by interpreting statistical results based on multivariate regression models, hypothesis testing, and confidence intervals.

  • Statsmodels for multivariate linear/logistic regressions
  • Packaging and object-oriented programming in Python
  • Notebook-based presentations with interactive graphs

What you will build in practice

  • 40h-long data consulting challenge based on real data from a marketplace
  • Individual presentations of your key findings to your client

Machine Learning

Implement the Machine Learning workflow with Scikit-Learn (data preparation, feature engineering, model selection, evaluation, and fine-tuning) and understand maths intuitions and numerical implementations of ML models.

  • Scikit-learn and XGBoost libraries
  • Supervised learning (linear, KNN, SVM, Trees, Ensembles)
  • Unsupervised learning (PCA, K-means, t-SNE, DBSCAN)
  • Structured data (tabular, time-series with SARIMAX...)
  • Unstructured data (images, text with Naive-Bayes, Tf-idf, LDA...)

What you will build in practice

  • Machine Learning models perfectly fine-tuned to your tasks
  • Pipelines combining data processing and model predictions
  • Image compression model by color clustering 
  • Spam detection algorithms
  • Prediction model for house prices

Deep Learning

Unveil the magic behind Deep Learning! Understand the architecture of neural networks (neurons, layers, stacks) and their parameters (activations, losses, optimisers). Build your own neural networks (dense, recurrent, or convolutional), to work on images, sequences and texts. Learn how to re-use and transfer learning from pre-trained “big-models”  from latest open-source research! Get your hands dirty with auto-encoders, batch data processing pipelines, and GPU training.

  • Scikit-learn and XGBoost libraries
  • Supervised learning (linear, KNN, SVM, Trees, Ensembles)
  • Unsupervised learning (PCA, K-means, t-SNE, DBSCAN)
  • Structured data (tabular, time-series with SARIMAX...)
  • Unstructured data (images, text with Naive-Bayes, Tf-idf, LDA...)

What you will build in practice

  • Dense neural network for fraud transaction detection
  • Transfer learning for image classification
  • Auto-encoders for image compression and denoising
  • Recurrent networks for wheather forecasting

Machine Learning Engineering (MLOps)

Turn your best handcrafted models into a replicable python package that can be trained on big data in the cloud, using virtual machines and online databases. Monitor your model performance as new data comes in, retrain it when needed, and expose its predictions to the world via APIs or websites.

  • VS code & command line
  • Google Cloud, Virtual Machines, SSH for the trainer
  • MLflow & Prefect for DAG orchestration
  • Docker & Fast API for the backend
  • Streamlit for the frontend

What you will build in practice

  • ML model for predicting taxi fares, trained on big data on the cloud with GPUs
  • Visual web dashboard showing live predictions (on charts, maps, etc.)
  • Trained models in production capable of self-healing
  • World embedding for sentiment analysis or text auto-completion

Projects

Projects Phase is the ultimate experience of the course. Collaborate efficiently in teams of 3-4 people on a real data science project that you will either join or pitch to your class. Use either open-data repositories (government initiatives, Kaggle, Paper with Code, etc...) or bring your own private dataset. With full-time mentoring from expert teachers, let your wildest dream come true!

What you will build in practice

  • Make an app with a a live demo of model predictions
  • Create an in-depth analysis of a business dataset
  • Replicate latest AI research papers with Big Models!

Career Weeks: start your career in data science!

Meet data science experts working in startups or large companies, prepare your CV and do mock interviews to prepare your job search. Go deeper into essential data topics.

  • Preparation for your job search
  • Connection to our 15,000 alumni and 985+ hiring partners

What you will do in practice

  • 1:1 coaching 
  • Review of CV and cover letter
  • Technical interviews preparation

Locations

Where would you like to study Data Science?

You can choose to learn Data Science in over 45 locations worldwide or online. Find now your learning destination!

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Bordeaux
Bordeaux
Lille
Lille
Lyon
Lyon
Marseille
Marseille
Nantes
Nantes
Nice
Nice
Paris
Paris
Rennes
Rennes

Amsterdam
Amsterdam
Barcelona
Barcelona
Berlin
Berlin
Brussels
Brussels
Cologne
Cologne
Lausanne
Lausanne
Lisbon
Lisbon
London
London
Madrid
Madrid
Munich
Munich
Porto
Porto
Zurich
Zurich

Bali
Bali
Melbourne
Melbourne
Shanghai
Shanghai
Singapore
Singapore
Tokyo
Tokyo

Buenos Aires
Buenos Aires
Mexico
Mexico
Montreal
Montreal
Rio de Janeiro
Rio de Janeiro
Santiago
Santiago
São Paulo
São Paulo

Cape Town
Cape Town
Casablanca
Casablanca
Mauritius
Mauritius
See all locations

Need more details about our Data Science bootcamp?

✔

Understand the goal of the bootcamp

✔

Get our syllabus week by week

✔

Understand our methodology

Coursereport

4.98 / 5

2259 reviews

Switchup

4.98 / 5

2301 reviews

Careerkarma_white

4.9 / 5

714 reviews

Download our Data Science syllabus
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Get hired at top tech companies

Le Wagon helps you land your dream job. Our career services guide you at the end of your bootcamp.

N26 Spendesk Qonto Trainline Microsoft BCG Getaround Metaverse Backmarket
Amazon Accenture Doctolib Apple Ernst and Young Shopify Hello Fresh IBM Lydia

985+

hiring partners

93%

employment rate

3 months

on average to find a job

Admission

How to apply to our Data Science bootcamp

Our Data Science course is very complete and intense. But don’t worry, if you don’t have the suggested requirements we will help you get there!

1

Suggested requirements

Programming: you'll need to be comfortable with data types & variables, conditions, loops, functions and data structures.
Mathematics: you'll need a High School level of Maths, meaning you will be comfortable with functions, derivatives, and systems of linear equations.
2

Book an interview with our enrolment advisor

When you apply, we'll get back to you to schedule a 30 minute video interview. We'll talk about your professional project and your motivation.
3

Pass our technical quiz

You will receive a Programming & Mathematics Quiz to help you and the Admissions team better understand your current level.
4

Payment options & prepwork

The last step will consist of finding the most suitable financing option for you. Then, you'll jump into the prepwork which consists of a 40h training.

Have questions?

✔

Choose the right course for you

✔

Explore your financing options

✔

Preview our learning platform

Coursereport

4.98 / 5

2259 reviews

Switchup

4.98 / 5

2301 reviews

Careerkarma_white

4.9 / 5

714 reviews

Book a free call with our advisor

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Financing options

Find the right financing
options for you

Finances shouldn't be a barrier to accessing our bootcamps. We're always finding new ways to facilitate payments and fundings.

See your financing options
Financing options

More than a bootcamp.
Join a global tech network for life.

By choosing Le Wagon, you are joining a supportive community of alumni, teachers, tech recruiters. Benefit from life-long access to the course material, and to our network job offers.

17,756

alumni

45

campuses

1,500

tech experts and professors

93k

meetup members

Need more details about our Data Science bootcamp?

✔

Understand the goal of the bootcamp

✔

Get our syllabus week by week

✔

Understand our methodology

Coursereport

4.98 / 5

2259 reviews

Switchup

4.98 / 5

2301 reviews

Careerkarma_white

4.9 / 5

714 reviews

Download our Data Science syllabus
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