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 Science student learning

Data Science Bootcamp

Choose location

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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Part-Time

2
2

weeks

remote

On-site / Remote

language

English

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%.

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

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

What you will learn

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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.
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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.
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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.
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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.
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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.
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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).
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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.
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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).
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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.
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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

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

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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.

Certificate

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.

    Details

  • 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.

    Details

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FAQs

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.
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