Data Science & AI Bootcamp

In the Data Science & AI Bootcamp from Le Wagon, you learn to analyze data. You build machine learning models and develop AI solutions. The goal is entry into roles such as Data Scientist, Data Analyst, AI Engineer, or Data Manager.

Before the start, you get approx. 30 hours of online learning material on Python, Git, and SQL. Additionally recommended is a live online introduction to Python and SQL with the instructors. After that follow the modules Data Analysis, Decision Science, Machine Learning, Deep Learning, and ML Engineering.

You work continuously on practical tasks with real data. These include a database of scraped bookshop data and a data consulting challenge with marketplace data. Also included is a spam detection, plus a model for taxi prices that is trained in the cloud. In the deep learning part, you program a transformer from scratch and customize your own LLM.

In the project weeks, you work in a team of three or four people on a real data science project. Also on the agenda are agile project management, AI ethics, model explainability, and CI/CD for machine learning. At the end, you present a live demo of your model.

You choose between full-time and part-time. Full-time means Monday to Friday, 9 a.m. to 6 p.m., approx. 40 hours per week. The part-time option has live sessions in the evenings and on weekends and amounts to approx. 16 hours per week. The course takes place on-site in Berlin or online.

The bootcamp is aimed at people with basic knowledge of programming and mathematics. The career service supports you with 1:1 coaching, interview training, and contacts with hiring partners. After completion, you receive a certificate in Data Science & AI.

• Preparation: approx. 30 hours of online tutorials on Python, Git, and SQL
• Data Science Introduction: fundamentals of Python and SQL, live online (40 hours)
• Data Analysis: Python for Data Science, SQL, statistics, and linear algebra (56 hours)
• Decision Science: data consulting challenge with regression models, hypothesis tests, and confidence intervals (32 hours)
• Machine Learning: the ML workflow with Scikit-Learn, from data preparation to fine-tuning (88 hours)
• Deep Learning: neural networks for image, sequence, and text, Transformers from scratch, GenAI applications (64 hours)
• ML Engineering: train, monitor, and deploy models as a Python package in the cloud via API (40 hours)
• LLMs and Gen AI: LLMs, RAGs, reinforcement learning, agents, evaluation, and GANs
• AI-first Python Coding: current best practices and Python programming with AI
• Neural Networks and Natural Language Processing
• Extended Learning: agile project management, AI ethics, explainability of models, CI/CD in Machine Learning
• Project weeks: your own data science project in a team of 3 to 4 people (80 hours)
• Career service: personal branding, training for technical interviews, 1:1 coaching
• Tools: Python, SQL, Jupyter, Pandas, NumPy, Matplotlib, Scikit-Learn, TensorFlow, Keras, Hugging Face, LangChain, MLflow, Docker, FastAPI