AI & Data Science Bootcamp
The CU AI & Data Science Bootcamp delivered by Constructor University in partnership with Constructor Nexademy is a 6-week intensive online program designed for working professionals who want to work confidently with data and AI.
Python, NumPy and Pandas to clean, structure and explore any dataset
Read data confidently, design experiments, and visualise the right story
From regression to clustering, know which model to use and why
Take a model from notebook to a real-world business decision
Use LLMs, RAG and AI agents to work faster and smarter
Query databases, understand MLOps, and present to a real jury
This program is designed for ambitious professionals who work with data every day but want to go further. Think of the analyst who wants to automate her reports, the consultant who wants to understand what the data team is actually saying, or the engineer who wants to build AI into his products. Though coding experience is not necessarily a prerequisite, please expect to get exposed to Python programming during the bootcamp. Motivation, curiosity, and the drive to learn quickly are what we're most looking for.
Language: English
First Cohort: September 2026
Certification: Certificate of Completion – Constructor University
Price: EUR 3,900 per participant
Three evenings per week, 3 hours per session. Wednesdays, Fridays, and weekends are free, designed to respect your career.
Monday | 18:00-21:00 Lecture + Exercise | Tuesday | 18:00-21:00 Lecture + Exercise |
Wednesday | Free | Thursday | 18:00-20:00 Group Work | 20:00-21:00 Q&A/Lab |
Friday | Free | Weekend | Self-study |
Content built on Nexademy's proven Data Science curriculum, condensed and reframed for working professionals. Delivered by CU faculty and Constructor Nexademy instructors.
Week 1 – Foundations
Python, Data Toolkit & Working with Data
- What is Data Science? The data-driven business mindset.
- Python fundamentals from scratch – no experience needed (variables, functions, data structures).
- NumPy and Pandas: loading, cleaning and manipulating real datasets.
- APIs and JSON: pulling live data from the web.
- Hands-on exercise: build your first data pipeline.
Week 2 – Statistics
Statistics, Visualisation & Storytelling with Data
- Probability, distributions and descriptive statistics – intuition over formulas.
- A/B testing and experimental design for business decisions.
- Data visualisation principles: how to choose the right chart.
- Interactive dashboards with Plotly and Streamlit.
- Hands-on exercise: tell a story with a real dataset.
Week 3 – Applied Machine Learning
Applied Machine Learning I
- What is Machine Learning? Intuitive explanation, no maths degree required.
- Supervised learning: regression (predicting numbers) and classification (predicting categories).
- Unsupervised learning: customer segmentation with clustering.
- Decision Trees, Random Forests, AutoML – how they work and when to use them.
- Model evaluation and explainability (SHAP).
- Hands-on: predict customer churn + internal Kaggle competition.
Week 4 – Applied Machine Learning
Applied Machine Learning II
- Taking your ML model from notebook to real-world application.
- How to frame a business problem as a data problem and solve it end to end.
- Working with a real dataset: data cleaning, modelling, interpretation.
- How to communicate model results to non-technical stakeholders.
- Group challenge: solve a real business case using the tools from Week 3.
- What makes a good model? Evaluation in a business context.
Week 5 – Generative AI
LLMs, Generative AI & Agentic AI in Practice
- How Large Language Models (ChatGPT, Claude, Gemini) actually work.
- Prompt engineering: getting reliable, business-grade outputs from AI.
- RAG (Retrieval-Augmented Generation): build AI that uses your company's data.
- Agentic AI: AI that takes actions, not just answers questions.
- Guest Session 1 – Real-world GenAI implementation in enterprise.
- Guest Session 2 – AI strategy, ethics and responsible deployment.
Week 6 – Capstone
Databases, MLOps & Final Project
- SQL for data professionals: query databases like a data scientist.
- From notebook to real product: intro to MLOps and model deployment.
- Capstone project: end-to-end data or AI solution for a real business problem.
- Team presentations in front of a jury (CU faculty + industry guests).
- Certificate of Completion ceremony – Constructor University Bremen.
Özge Güner
International Project Manager
Professional & Executive Education, Constructor University
Email: oguner@constructor.university
Phone: +90 544 730 22 36