Part-Time Data Science Bootcamp | Constructor Learning

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

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

Students say

Lina Siegrist-Choo

Data Science

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

Before: Postdoctoral Researcher

After: Junior Data Engineer at Nestlé

Tiffany Carruthers

Data Science

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

Before: Data Engineer

After: Data Engineer at Axpo

Seth Dow

Data Science

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

Before: Math Teacher

After: Data Analysis at Migros

Where our students work

You can work here too! Get your dream job, we will help 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.

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

Click here to 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.

Tools we teach

Python

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

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.

SQL

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

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

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

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.

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

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.