Lectures
Watch engaging & interactive video-recorded lectures online at a time that suits you, before putting what you’ve learned into practice.
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Course info
Master the fundamental skills of a data scientist
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.
Pandas
Numpy
Statsmodels
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-Learn
Tensorflow
Google 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.
Git
Docker
MLflow
FastAPI
You can start for FREE! Join a live workshop for web development, data analytics, or UX design.
What our Data Science bootcamp looks like
You can study Flex part-time or Full-time. Check out what our curriculums cover.
Prepwork: get ready to start the bootcamp!
Our Flex part-time Data Science Bootcamp is a fully remote program that requires online study. To ensure you are thoroughly prepared, you must first complete ~40 hours of intuitive and interactive online learning. This prep work will ensure that you have the necessary foundation in Python and mathematics before the bootcamp begins.
- Setup your learning environment (notes, text editor, etc.)
- 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
- Study curated resources from Le Wagon to build a solid foundation
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
Online 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 online with 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 online coaching
- Review of CV and cover letter
- Technical interviews preparation
Prepwork: get ready to start the bootcamp!
Once you’ve been accepted onto the online course, you'll receive ~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
- 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
Online Project Weeks
Projects Phase is the ultimate experience of the course. Collaborate efficiently in online 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 online with 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 online coaching
- Review of CV and cover letter
- Technical interviews preparation
Need more details about our Data Science bootcamp?
Understand the goal of the bootcamp
Get our syllabus week by week
Understand our methodology
4.98 / 5
2259 reviews
4.98 / 5
2301 reviews
4.9 / 5
714 reviews
Download our Data Science syllabus
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An immersive online program that accelerates
your tech skills in weeks
Discover
Discover our thriving community and online learning ecosystem.
Connect with like-minded individuals from around the world - enjoy the ride, build your community, and fast track your career!
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What was your favourite thing about Le Wagon Online?
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Learn to code from home | A day in the life of a Le Wagon Online Student
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Start your tech career remotely with Le Wagon Online
Career services
Land your dream job with our career team
Benefit from personalized coaching, attend job fairs, get prepared for interviews, connect with recruiters.
985+
hiring partners
93%
of graduates find jobs
3 months
on average to find a job
See what our Data Science
graduates do now
Financing
Do you need help with financing?
Understand your eligibility and learn how to finance your bootcamp.
- Installment plan - Spread out your tuition payments.
- Public funding - Get your bootcamp financed by government funding and subsidies.
- Loans - Apply for a loan with one of our partners.
- Scholarships - Benefit from social impact programs and discounts for underrepresented communities.
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.
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
Choose your learning schedule: Flex part-time or Full-time
4.98 / 5
2259 reviews
4.98 / 5
2301 reviews
4.9 / 5
714 reviews
Book a free call with our advisor
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