Data Science Bootcamp

Boost your career with our 22-week part-time Bootcamp and learn new skills in Python, Machine Learning, Deep Learning and NLP.

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Data Science student learning





On-site / Remote



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 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. In addition, our part-time program allows you to continue working 100%.

Awarded as one of the best Data Science bootcamps worldwide

Constructor Learning's Data Science bootcamp has been recognized as one of the best in the world.

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Upcoming dates

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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

Where our students work

You can work here too! Get your dream job, we will help you along the way

Swiss International Air Lines

What you will learn


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 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.

Data Science toolkit weeks 1-3

  • 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.

Data visualization weeks 4-5

  • 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.

Statistics & experimental design weeks 6-7

  • 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.

Classical & advanced Machine Learning (ML) weeks 8-11

  • 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).

Deep Learning weeks 12-14

  • 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) weeks 15-17

  • 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 week 18

  • 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 19-22

  • 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.

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

Weekly schedule














* The course takes place every second Saturday.

Schedule doesn't fit your needs? Check out our Full-Time program.


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


Work 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.

Tools we teach

For many reasons, the fastest-growing programming languages globally: its ease of learning, the recent explosion of the Data Science field, and the rise of Machine Learning. Python also supports Object-Oriented and Functional Programming styles, which facilitate building automated tasks and deployable systems. There are plenty of Python scientific packages for Data Visualization, Machine Learning, Natural Language Processing, and more.
TensorFlow is an open-source software library for Deep Learning developed by Google. Across a range of tasks, it can build and train neural networks to detect and decipher patterns and correlations analogous to the learning and reasoning that humans use. It is highly optimized to run in computational servers where task parallelization is possible.
Relational Database Management Systems (RDMS) are present in any kind of data-oriented system. RDMS are comprised of columns and rows to store data within a structured format and are a potent tool to store massive amounts of information. SQL is the language to query and manipulate data in RDMS and is, for this reason, very relevant in the field of Data Science.
Scikit-learn is the most established and developed Machine Learning library. It features various classification, regression and clustering algorithms, including support vector machines, random forests, gradient boosting, k-means, and DBSCAN. It directly interoperates with the numerical and scientific libraries NumPy, Pandas, and SciPy.
Git is a free and open-source version control system designed to handle everything from small to huge software projects (Linux kernel). It allows you to keep track of the code changes and collaborate with others on a project. You will use it daily during our Data Science course.
NumPy & Pandas
NumPy is the fundamental package for scientific computing with Python, adding support for large, multi-dimensional arrays, along with an extensive library of high-level mathematical functions. Pandas is a library build on top of NumPy for data manipulation and analysis. The library provides data structures and a rich set of operations for manipulating numerical tables and time series.
Cloud Services
More and more companies are moving to cloud computing systems. It has become the primary location for businesses to store data, train ML models, and deploy production systems. Having experience with a cloud system will make you stand out in today's job market.
PyCaret is an open source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within minutes. It also has Automated ML capabilities to build a suite of trained models in minutes on any problem and assist with model selection and tuning.

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.


Our capstone projects

What clearly sets us apart from other Bootcamps 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

How your final project could look like


Brain MRI Classification in collaboration with Kantonsspital Winterthur

Data Science

Project by: Cornelia Schmitz, Norbert Bräker

More info

Using AI for automatic feature prediction from product images

Data Science

Project by: Valeria Polozun, Seth Dow

More info

Classification of NOTAMs for SWISS International Airlines using AI

Data Science

Project by: Jean Coupon

More info

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.


Choose your location

Join us from everywhere in the world

We offer our courses at different locations. Learn remotely from anywhere in the world or attend on-site at one of our locations. Click on your preferred location to learn more.

Upcoming events

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

  • Final presentations

    17. Feb 23, 06:00 PM - 08:00 PM GMT+1

    Heinrichstrasse 200, 8005 Zurich or online via Zoom

    Join us on Friday, February 17th, 2023, at 6 PM to see what our graduates have been up to in the final weeks of their bootcamp. Our talented students have been working hard to develop innovative solutions to real-world problems using Data Science and Full-stack development skills. You'll have the opportunity to hear from them as they present their projects and share their insights and experiences. Take advantage of this exciting event! Register now to reserve your spot. We hope to see you there!


  • Introduction to HTML & CSS workshop

    21. Feb 23, 05:00 PM - 06:30 PM GMT+1

    Online via Zoom

    Join us for our upcoming online HTML and CSS workshop on Tuesday, February 21st from 5-6:30 PM. Our experienced instructor will guide you through the fundamentals of HTML and CSS, including positioning, Flex, animations, etc. then take what you have learned and dive into a couple of exercises. Whether you are a beginner or have some experience with HTML and CSS, this workshop will provide valuable tips and techniques to improve your web development skills. Don't miss out on this opportunity to learn from the best and take your web development career to the next level. Space is limited, so register now to secure your spot. We hope to see you there!


  • Data analytics workshop

    23. Feb 23, 05:00 PM - 07:00 PM GMT+1

    Online via Zoom

    Join Dipanjan on Thursday, February 23, 2023, 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. He 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.


  • Final presentations - part-time students

    24. Feb 23, 06:00 PM - 08:00 PM GMT+1

    Heinrichstrasse 200, 8005 Zurich or online via Zoom

    Join us on Friday, February 24th, 2023, at 6 PM to see what our part-time students have been up to in the final weeks of their Bootcamp. Our talented students have been working hard to develop innovative solutions to real-world problems using Data Science and Full-stack development skills. You'll have the opportunity to hear from them as they present their projects and share their insights and experiences. Take advantage of this exciting event! Register now to reserve your spot. We hope to see you there!


  • Online information session

    28. Feb 23, 06:00 PM - 07:00 PM GMT+1

    Online via Zoom

    Join us on Wednesday, February 28, 2023, from 6 PM until 7 PM for an exciting information session about Constructor Learning and our programs. Whether you're a beginner looking to learn a new skill or an experienced professional looking to advance your career, this information session is the perfect opportunity to learn more about our bootcamps and how they can help you reach your goals. This online information session will provide an overview of our programs, including the curriculum, career outcomes, and admissions process. You'll have the opportunity to ask questions and get insights from our instructors. Don't miss this opportunity to take the first step towards a rewarding new career in Data Science, Full-stack development, or UXUI design. Sign up now to reserve your spot in the information session. See you soon!


  • Learn how to code with Python

    15. Mar 23, 06:00 PM - 07:30 PM GMT+1

    Online via Zoom

    Join us for our upcoming online Introduction to Python programming workshop on Wednesday, March 15th, 2023, from 6 - 7:30 PM. Whether you're new to programming or looking to expand your skills, this workshop is the perfect opportunity to learn the basics of Python programming and take your skills to the next level. Led by expert Python programmer Ansam Zedan, you will be guided through the fundamentals of programming in Python, including variables, conditions, and loops. But that's not all - you'll also have the chance to personalize your own turtle and give it commands to create unique drawings on the screen. By the end of the workshop, you will have a solid understanding of Python programming and the ability to create your own digital masterpieces. Don't miss out on this exciting opportunity - register now to secure your spot! We can't wait to see you there.


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.

When do I have to pay the tuition fee for the part-time Bootcamps?

Upon enrollment, you are required to pay a non-refundable CHF/EURO 3,500 deposit to reserve your seat in the program. 1/2 of the remaining balance is due by the end of the second week of the program and 1/2 by the third month of program.

What's the course schedule for the part-time Bootcamp?

The part-time Bootcamp is a 22-week program, with lectures every Tuesday and Thursday from 6pm - 9pm and every other Saturday. In addition, our students invest a few extra hours of their free time to review what they have learned and work on projects.

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


Team Member

Marcus Lindberg

Data Science Program Manager

Starting his career in clinical immunotherapy research, Marcus was exposed to the pressing need for better ways to make sense of patient data. With a growing interest in personalized therapies, he pursued a MSc in Bioinformatics at the University of Edinburgh and joined ETH Zürich’s Clinical Bioinformatics Unit. Now at SIT Learning, he is able to keep refining his analytical toolbox while helping people reach their goals along the way.
Team Member

Dr. Mark Rowan


What drives you? For me, it's about using data to tell a story and change the world. Whether it's neuroscience, aerospace, telecoms, insurance, or voice tech - I love getting into the data and making things happen.
Team Member

Gerry Liaropoulos


As an experienced Data Scientist in the fascinating sector of Life Sciences, I am using a variety of Machine-Learning methods to help the industry make more informed decisions with the end goal of effecting a positive change on patients’ lives.
Team Member

Patrick Senti

Freelance Analytics Consultant

Patrick has been building analytics solutions since 1995, applying machine learning, data engineering, data analytics & visualization. Helping customers in the finance, transportation and retail industries his experience includes software engineering & architecture in distributed systems from enterprise backends to mobile & IoT systems. Senior BI/Data Science & Software Engineer since 1995 * Applied data science, data engineering, software engineering, big data * Wide industry experience in Finance, retail, logistics Roles * Data scientist/data & ML engineer, software engineering, consulting * Lead Data Analytics Practice at swissQuant * Senior Software Engineering, Tech Lead at Credit Suisse, Logicalis, SAS, IBM Education * CAS ETH Zürich in Computer Science & Distributed Systems * Swiss Dipl. Business Informatics (Professional Master) * Executive MBA Freelance Analytics Consultant, Founder Helping companies to productize and operationalize ML
Team Member

Dr. Ekaterina Butyugina

Data Science Program Manager & Instructor

Ekaterina studied mathematics at university and worked as Junior Researcher in Russia where she did her PhD in Continuum Mechanics. Looking for the opportunity to find something close to science but more dynamic and applicable to real life, she joined the Data Science program, then stayed on as a TA and later joined the team as a Data Science Consultant. She likes to work with data and apply both analytical and creative approaches, trying new techniques and sharing them with other people.

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