Part-Time Data Science Bootcamp & Intensive Program in Zurich

Program overview

Do you want to build on your existing skills to advance your career, learn new technologies, or get back into the workforce after a long break? In any case, our Intensive Program is exactly what you are looking for. We have carefully designed our curriculum to contain the most up-to-date tools currently in demand in the job market. In addition, our part-time program allows you to continue working 100%.

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Upcoming Dates

Zurich

Munich

Bremen

Remote

Course dates

Aug 19 - Jan 23

Apply by

Jul 29

Tuition

12'700 CHF

Format

On-site

Apply

Schedule doesn't fit your needs?
Check out our remote options or the Full-Time program.
Looking for financing? Check out our financing options.

Schedule

Tue
Remote
- 18.00 - 21.00Lecture
Thu
Remote
- 18.00 - 21.00Practice
Sat *
On-site
- 09.00 - 12.00Lecture
- 13.00 - 16.00Practice
* The course takes place every second Saturday.

LectureLearn from our instructors who are experts in their respective fields and get introduced to new topics during live lectures.

PracticeWork on a set of interesting and challenging exercises related to the topics covered during the previous lecture. Practice your team-building skills by doing group projects together with your peers.

Where our students get jobs

Get your dream job - we'll support you along the way!

Our Alumni Stories

Thi Tuyen Nguyen

Data Science

❝The intense curriculum of the bootcamp pushed me outside my comfort zone, enhancing my resilience and passion for continuous learning while equipping me with essential skills for a transformative career in data science.❞

Before: Postdoctoral Researcher

After: Artificial Intelligence Intern at Baader Bank AG

What you will learn

After applying
Preparation work
Our Data Science course is very demanding and intensive. Therefore, we have put together a preliminary course that specifically prepares you for it. Depending on your previous knowledge, this requires about 1-2 weeks of intensive work.
- Learn about statistics, basic probability, calculus and linear algebra, version control, and Python.
- If needed, our team is on call via Discord to support you.
Week before start
Open session
Meet your fellow students for an evening session the week before the program starts. Review the preparation work and exchange your problems and solutions with the class.
Week 1 - 3
Data Science toolkit
- Learn the tools and programming languages relevant to Data Science.
- Python fundamentals for Data Science, version control (git and GitLab), SQL databases, organizing and structuring data science projects.
- In depth data wrangling in Python (accessing online data through APIs, data cleaning and exploration with Pandas).
- Work with both JupyterLab and integrated development environments.
Week 4 - 5
Statistics & experimental design
- Use statistical methods to assist decision-making using critical methodologies like A/B testing.
- Apply inferential statistics, parameter estimation and hypothesis testing on Data Science problems.
- Learn about probabilistic modeling and generalized linear models and solve real-world problems.
Week 6 - 7
Data visualization
- Use advanced visualization techniques for extracting actionable insights from data and create visually compelling stories.
- Create interactive figures and even full-fledged dashboards leveraging tools like Matplotlib, Seaborn, Plotly and Dash.
Week 8 - 11
Classical & advanced Machine Learning (ML)
- Build advanced end-to-end machine learning pipelines.
- Gain an in-depth view of supervised learning methods (regression and classification), as well as unsupervised learning methods (clustering, outlier detection, and dimensionality reduction).
- Learn ML core concepts (ex: gradient descent, linear vs non-linear models, loss functions, cross-validation, tuning).
- Solve real-world scenarios including: tackling imbalanced data, selecting suitable models, optimizing model performance using hyperparameter tuning, and model interpretation using frameworks such as LIME and SHAP.
- Learn about the most recent advancements, applications and frameworks for Auto-ML (PyCaret, TPOT and Auto-Sklearn).
Week 12 - 14
Deep Learning
- Learn the theory and history behind neural networks and deep learning.
- Build your own networks using TensorFlow and Keras - Artificial Neural Networks and Convolutional Neural Networks.
- Use deep transfer learning and state-of-the-art Deep Learning models to solve computer vision problems like image classification and segmentation.
- Interpret and explain deep learning models for vision using techniques like Grad-CAM.
Week 15 - 17
Natural Language Processing (NLP)
- Learn NLP core concepts (e.g.: named entity recognition, topic modeling, document classification, similarity, embeddings, etc.).
- Learn and practice how to transform unstructured text into structured features and train classical ML models.
- Solve diverse problems like classification, recommendation, summarization, named entity recognition, and more.
- Use Deep Learning models and Transfer Learning including Transformers to solve more complex tasks (language translation, contextual similarity, semantic search, and more).
- Learn about Generative AI for NLP, Prompt Engineering and Large Language Models (LLMs) like ChatGPT to solve diverse NLP tasks including QA Chatbots.
Week 18
Machine Learning Engineering
- Learn how to approach a Data Science project effectively by using conventional workflows and creating a clean project structure.
- Learn about MLOps best practices such as model & data version control, experiment tracking, model and code testing and CI/CD for ML projects.
- Use Docker containerize and serve your model, making it accessible via an API that you will deploy on a cloud server.
Week 19 - 22
Capstone project
- Solve real Data Science problems from our carefully curated list of pre-defined projects or even better, bring your own data and Data Science problem!
- Experience the complete Data Science process: from defining your business problem, exploring the data, applying suitable machine learning techniques, to finally delivering a functional prototype.
- Get coached and present your work in a public meetup.

Application process

Apply to the program here
Send us your CV or LinkedIn profile
First motivational interview with Constructor Academy
Prepare for the technical interview
Pass the technical interview
Pay a deposit to secure your spot
Complete your preparation work before the Intensive program starts

Get ready for the course

Free Data Science intro course

Online

Self-paced

Free of charge

Learn about Python, the data science project lifecycle, and practice on a real-world data science problem in this free self-paced online tutorial. By completing this course, you will gain a better understanding of the Data Science world and increase your chances of being accepted into the Intensive program.

Estimated time to complete: 15 hours

Start learning

Topics

Data Analytics

Examine large and complex data sets to uncover insights, trends, and patterns that can inform decision-making.

Python SQL Numpy Pandas Jupyter Notebook Matplotlib Streamlit

ML & AI

Train computer algorithms to learn patterns and make predictions or decisions without explicit instructions, based on data inputs.

ChatGPT OpenAI

LangChain Scikit-learn SciPy TensorFlow PyCaret

DevOps

Efficiently manage team tasks and collaborate using GitLab. Gain the ability to deploy your applications on the web and seamlessly connect them to each other.

GIT GitLab Docker AWS

Python

Python is taking over the world!

Python is the market leader in many sectors:

Data Analytics
Machine Learning
Artificial Intelligence
Scientific Research
Software Prototyping
Generative AI
And more...

Hands-on

Over 360 hours of hands-on training

Take part in the AI revolution!

Our instructors

One of our biggest assets is our instructors. Besides our internal Data Science team, we always bring in selected external experts from industry. These external instructors keep us in constant contact with trends and requirements in industry and allow us, as well as yourself, to build a well-established network. We really care about selecting instructors with outstanding didactic skills and constantly improving our teaching based on your feedback. Have a look at our impressive team of instructors and their diverse backgrounds.

Instructors

Our capstone projects

What clearly sets us apart from other courses is that we organize REAL projects with REAL companies. We do not rest when it comes to finding companies who can provide exciting projects for you and your course mates. This gives your portfolio a big push, and you wouldn't be our first student who might get hired by one of these companies after the project. Also, we are not shy! If you are interested in a particular company, we are very happy to contact them to see whether we can start a project together.

Final projects

Finish your professional transformation by working on an industry relevant capstone project.

Preparation phase

Organize your project

Receive and/or set the requirements
Set milestones

Development/Creation phase

Work in a team

Use collaborative tools
Split and coordinate different tasks
Learn from your fellow teammates
Build your first real world project

Presentation

Leave your first mark in the industry

Present your capstone project with your team mates in front of attendees from our network.

Sign up for the next final presentations on .

Data Science

One step ahead: Detecting unusual human motions

Project by:
Alaa Elshorbagy, Vincent von Zitzewitz, and Jonas Voßemer

Project description

See full list.