Data Science & AI Bootcamp

In the "Data Science & AI Bootcamp" you learn the complete path from raw data to the finished AI application. It starts with a pre-work chapter on Python, environments like Anaconda and Jupyter, SQL, linear algebra, as well as Git and GitHub.

After that, you work with Pandas on data analysis and data preparation: missing values, outliers, imbalanced data and visualization. This is followed by classic machine learning with supervised and unsupervised methods, evaluation metrics, cross-validation and hyperparameter tuning.

In the deep learning part, you build neural networks with PyTorch. This is followed by two specializations. One covers computer vision with CNNs for classification, object detection and segmentation. The other is dedicated to natural language processing with embeddings, RNNs, attention and transformers. A chapter on LLM applications covers prompt engineering, LangChain and retrieval-augmented generation. Finally, it's about deployment with FastAPI, Docker, Ray and MLflow.

The course is entirely online and in English. Live sessions with instructors alternate with self-study time, and the flipped classroom method is also used. Full-time is 40 hours per week (22.5 hours live), part-time is 20 hours per week (9 hours live). Each chapter ends with a practical project for your portfolio.

According to the provider, you do not need prior knowledge in computer science. Basic computer skills and a basic understanding of programming are helpful. You should have at least B1 level English.

If you complete all tasks and projects, you receive a Certificate of Completion, otherwise a Certificate of Attendance. According to the provider, the certificate is AZAV-accredited. The bootcamp includes a career service week at the end and 1:1 career counseling up to six months after completion.

• Introduction to Python
• Environments (Anaconda, Jupyter Notebooks)
• SQL and Databases
• Linear Algebra
• Git and GitHub
• Data Acquisition, Exploration, Cleaning and Preparation
• Data Visualisation
• Feature Extraction, Selection, and Dimensionality Reduction
• Classical Machine Learning: Supervised and Unsupervised Learning
• Evaluation Metrics, Model Evaluation and Selection
• Introduction to Artificial Neural Networks
• Introduction to PyTorch
• Advanced Concepts in Deep Learning
• Introduction to Computer Vision and Convolutional Neural Networks
• Project: Image Translation using GAN
• Introduction to Natural Language Processing, Representations and Embeddings
• Recurrent Neural Networks, Attention and Transformers
• Introduction to LLM Applications
• Project: Building a RAG Pipeline with LangChain and Gemini
• Model Deployment, Monitoring Models and Deployment Challenges