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If you're an Indonesian citizen, receive a USD 1,000 discount on the total tuition fees. At Le Wagon Indonesia, we believe in making technology accessible to develop local talent who will grow the Indonesian tech ecosystem.
Join the next batch of our Data Science & AI course to start your journey into tech!
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
Hugging Face
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
ChatGPT
In this Data Science & AI course, you will learn the fundamentals you need to become a Data Scientist in just 9 weeks.
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.
40 hours of online tutorials
Curated resources from Le Wagon to gain solid foundations
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.
A database built by scraping data from online bookstores
Advanced analysis of football performances in SQL
Visual dashboard connected to stock-market APIs
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.
40h-long data consulting challenge based on real data from a marketplace
Individual presentations of your key findings to your client
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.
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
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.
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
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.
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
Through a series of live lectures, harness the capabilities of Generative AI tools to streamline your coding workflows and explore vital ethical considerations in AI. Gain insights into making complex AI models comprehensible to stakeholders and focus on the implementation of effective CI/CD pipelines.
Enhance your coding efficiency using Generative AI tools
Navigate the AI landscape ethically
Make complex AI models understandable using techniques like Shapley Values
Implement effective CI/CD pipelines
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!
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!
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.
1:1 coaching
Review of CV and cover letter
Technical interviews preparation
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