SIT Learning becomes Constructor LearningRead more

From our founding as Propulsion Academy in 2016 to our acquisition by SIT last year, today we welcome our new brand name: Constructor Learning. Constructor Learning is part of the Constructor Group, initially named Schaffhausen Institute of Technology (SIT). The organization was founded in 2019 by Dr. Serg Bell, a long-time entrepreneur and investor in technology and education. Dedicated to creating knowledge through science, education and technology, the ecosystem combines a comprehensive educational offering that spans the entire learning lifecycle, from K-12 to a private university and executive courses, next-generation research capabilities and commercial activities for technological innovation. Founded and headquartered in Schaffhausen, SIT has rapidly grown since its creation, thanks to organic growth and acquisitions. As it has become a global organization with a footprint in more than 15 countries and a worldwide network of researchers, professors, investors, clients, and alumni, the brand had to be rethought to reflect this expansion better and unify the entire ecosystem under one name: Constructor.

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Data Scientist

Data Science Bootcamp

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Become a Data Scientist in 12 weeks by acquiring the required knowledge in Python, Machine Learning, Deep Learning, and NLP. Solve an industrial data problem for the Capstone project.






On-site / Remote



Program overview

Recent graduate, entrepreneur, or you want to expand your existing skill set? In any case, our Bootcamp 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. This is what makes our Data Science Bootcamp innovative and what will enable you to take the next step in your career.

The #2 ranked Data Science Bootcamp globally

According to SwitchUp, Constructor Learning is considered the #2 Data Science Bootcamp in the world.

course report award
switchup award

Upcoming dates

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Schedule: Mo - Fr, 09:00 - 18:00 (CET)

You need to select a location to see upcoming dates and prices.

What you will learn


Preparation work (1-2 weeks)

To get the best out of our Data Science course good preparation is key. Therefore, we have put together a preparation 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 Slack to support you.


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.


Statistics & experimental design (6 days)

  • 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.


Data Science toolkit (6 days)

  • Learn the tools and programming languages relevant to Data Science.
  • Python fundamentals for Data Science, version control (git and GitLab), 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.


Data visualization (4 days)

  • 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.


Machine Learning I (4 days)

  • Gain an in-depth view of supervised learning methods (regression and classification).
  • 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 and selecting suitable models.
  • Build advanced end-to-end machine learning pipelines.


Machine Learning II (5 days)

  • Optimize model performance using hyperparameter tuning.
  • Use model interpretation frameworks such as LIME and SHAP.
  • Apply unsupervised learning methods (clustering, outlier detection, and dimensionality reduction).
  • Learn about the most recent advancements, applications, and frameworks for Auto-ML (PyCaret, TPOT, and Auto-Sklearn).


Deep Learning (5 days)

  • 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.


Natural Language Processing (NLP) (4 days)

  • 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 data and train classical ML models.
  • Solve diverse problems like classification, recommendations, summarization, named entity recognition, and more.
  • Use the latest state-of-the-art Deep Learning models, including transformers to solve more complex tasks (language translation, contextual similarity, search, and more).


Machine Learning Engineering (6 days)

  • SQL is one of the most requested job interview skills. In 3 days, we bring you from a complete beginner to an advanced level so that you are well prepared for your future job interviews.
  • 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.


Capstone project (weeks 9-12)

  • Solve real Data Science problems provided by companies and research institutions.
  • 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.

Get ready for the course

Free Data Science intro course

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 Bootcamp.

Estimated time to complete: 15 hours

Students say

Lina Siegrist-Choo

Lina Siegrist-Choo

Data Science

I can definitely say that I might not be able to achieve my career plan without joining Constructor Learning.

BeforePostdoctoral Researcher

AfterJunior Data Engineer at Nestlé

Tiffany Carruthers

Tiffany Carruthers

Data Science

After completing the Bootcamp, I was able to land a job through Constructor's professional network.

BeforeData Engineer

AfterData Engineer at Axpo

Seth Dow

Seth Dow

Data Science

I believe that the personnel at Constructor are top notch, and they are invested in your success.

BeforeMath Teacher

AfterData Analysis at Migros

Application process

Send us your CV or LinkedIn profile

First motivational interview with Constructor Learning

Prepare for the technical interview

Pass the technical interview

Pay a deposit to secure your spot

Complete your preparation work before the Bootcamp starts


Earn a Certificate of Accomplishment

Share your Certificate on social networks, printed resumes, CVs or other documents. Boost your career with the new skills that you gained.


Upcoming events

Attend one of our events. Discover our upcoming workshops, info sessions, final presentations and webinars on trending topics.

  • Data Analytics Workshop

    08. Dec 22, 05:00 PM - 07:00 PM GMT+1

    Location: Online via Zoom

    Join Dipanjan on December 8th from 5 - 7 PM and get an introduction to data analytics. Dipanjan is our lead data science consultant & instructor, leading advanced analytics efforts around Computer Vision, Natural Language Processing and Deep Learning. Dipanjan will lead you through python and data processing basics, talk about framing data science problems, and briefly discuss how to analyze and visualize unique patterns. At the end of the workshop, you will create a model that can predict housing prices using machine learning. If you are interested in data science and data science-related topics, this event is for you. Register today to save your seat.


  • UX/UI workshop with Kenji

    18. Jan 23, 05:00 PM - 06:30 PM GMT+1

    Location: Online via Zoom

    Join Kenji on Wednesday, January 18th, 2023, from 5 - 6:30 PM and get an introduction to prototyping in Figma. Kenji Nguyen is a UX designer at Ginetta, and an expert at designing business applications, custom websites, and mobile apps for leading corporations, ambitious SMEs, and innovative startups. Kenji will be hosting an interactive workshop on prototyping in Figma. This will include wireframing for the initial stages of UX and prototyping in Figma to give a basis for UI. If UX/UI design is something you are interested in exploring further, then this is the perfect opportunity for you! Register today to save your seat.


Empty room with chairs


What’s the non-technical interview?

Lasting 20 minutes in-person or over video call, it gives us a chance to get to know you, your professional experience, motivation and goals for participating in the program.

How many students are there per class?

To maintain a high level of interaction and instruction, each class has an average of 10 to max. 20 students (in-class).

Is the duration of the Bootcamps long enough?

Absolutely. For the Full-Stack and Data Science programs, 12 weeks of intensive practice (40 hours in the classroom with an additional 20-30 for course work per week) will give you what it takes to step into one of these fields.

What coding level do I need?

Though coding experience is not necessarily a prerequisite, we expect you to have been exposed to programming before, whether in industry, academia, or self-study. Motivation, hard-work, and drive are what we're most looking for.

I’d rather participate from another location. Can I attend the program remotely?

Absolutely. For those interested in this option, please select it on the application form.

Is there a difference between the in-person and remote option?

None at all. You’ll be joining the in-class participants for the same program and follow via our live stream platform. You’ll get the same attention from our staff as if you were on site.

What’s the technical interview like for the Data Science program?

The candidate will receive an email with a list of Python tutorials to complete before the interview. The interview date and time will be set such that there is around one week to get prepared for it.
On the day of the interview, the candidate will receive a data challenge by email and will have 2 hours to work on it. After submitting the results, a Constructor Learning team member will connect to discuss the results of the Data Challenge (around 15 min). Subsequently, a 30 minute Python coding assessment is conducted to determine the candidate’s structural and logical thinking. The whole process will take 2 hours, 45 min and be based on the tutorials sent before.
Contact us

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