Part-Time Data Science Course 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 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.

Upcoming dates

Zurich

Munich

Bremen

Remote

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Course dates

Tuition

The next date is not yet known

Schedule: Tue & Thu 18:00 – 21:00 and every second Sat 9:00 - 16:00 (CET)

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

Looking for financing? Check out our financing options.

Students say

Lingxuan Zhang

Data Science

❝"I decided to research some online data science courses, but I feel like most of them just give you a piece of the knowledge. I was specifically looking for a program that offered systemic learning. Then I found SIT Learning. Their unique curriculum is why I choose the Data Science Bootcamp."❞

Before: Ph.D. in transportation planning and management

After: Data Scientist at Saporo

Anselme Borgeaud

Data Science

❝“Looking back these were three incredible months in Zurich, and I would definitely recommend it to anyone considering joining the boot camp.”❞

Before: Ph.D. in Seismology

After: Asset Management at Pictet Group

Tiffany Carruthers

Data Science

❝After completing the Bootcamp, I was able to land a job through SIT’s professional network.❞

Before: Data Engineer

After: Data Engineer at Axpo

Lingxuan Zhang

Data Science

❝"I decided to research some online data science courses, but I feel like most of them just give you a piece of the knowledge. I was specifically looking for a program that offered systemic learning. Then I found SIT Learning. Their unique curriculum is why I choose the Data Science Bootcamp."❞

Before: Ph.D. in transportation planning and management

After: Data Scientist at Saporo

Where our students work

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

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.

Application process

Apply to the program

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

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 Bootcamp.

Estimated time to complete: 15 hours

Start learning

Weekly schedule

(CET)

Mo

Tue

Wed

Thu

Fr

Sat*

09H00

12H00

13H00

16H00

18H00

21H00

Remote

On-site

* The course takes place every second Saturday.

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

Lecture

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

Practice

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.

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.

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
And more...

Hands-on

Hundreds of 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 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

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 28. Sep 23.

brain-mri-classification-kantonsspital-winterthur

Data Science

Brain MRI Classification in collaboration with Kantonsspital Winterthur

Project by:

Cornelia Schmitz, Norbert Bräker

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.