Data Science Course | Constructor Learning

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.

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

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

Students say

Claudia Annoni

Data Science

❝The bootcamp was comprehensive and interesting with lots of practical exercises and the instructors were always available to answer our questions. It was definitely worthwhile.❞

Before: Senior Methodologist

After: Statistician at Office for National Statistics

Karim Khalil

Data Science

❝The overall experience was quite positive, and I must say that the bootcamp transformed my knowledge in the field.❞

Before: Data Scientist

After: Data Engineer at UBS

Lingxuan Zhang

Data Science

❝Their unique curriculum is why I choose the Data Science Bootcamp.❞

Before: PhD. 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 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.

1

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.

2

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.

3

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.

4

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.

5

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).

6

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.

7

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).

8

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.

9

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.

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

18H00

On-site

Schedule doesn't fit your needs? Check out our Part-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 morning 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

Over 370 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

How your final project could look like

brain-mri-classification-kantonsspital-winterthur

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

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.

We've got your back!
We like giving you individual attention, which is why you will have several one-on-one sessions throughout the Program to speak with our program manager or one of our instructors.

We support you to find your next dream job

Individual progression sessions
One-to-one sessions with career advisors
Cover Letter and CV writing sessions
Sending your CV to our network of hiring companies
In house events such as our Hiring Day
Opportunity to collaborate with companies on a project