Data Science Bootcamp
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Become a Data Scientist in 12 weeks by acquiring the required knowledge in Python, Machine Learning, Deep Learning, and NLP. Solve an industrial data problem for the Capstone project.
Full-Time
weeks
On-site / Remote
English
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.
The #2 ranked Data Science Bootcamp globally
According to SwitchUp, SIT Learning is considered the #2 Data Science Bootcamp in the world.
Upcoming dates
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Schedule: Mo - Fr, 09:00 - 18:00 (CET)
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What you will learn
0
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.
0
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.
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
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
Lina Siegrist-Choo
Data Science
"I can definitely say that I might not be able to achieve my career plan without joining SIT Academy."
Before: Postdoctoral Researcher
After: Junior Data Engineer at Nestlé
Jean Coupon
Data Science
"I particularly appreciated the attention the SIT team paid to the students, during the training but also afterwards, when there is a crucial need for support to find a job."
Before: Researcher Astrophysics
After: Data Scientist at Pictet Group
Application process
Send us your CV or LinkedIn profile
First motivational interview with SIT 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.
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.
How many students are there per class?
To maintain a high level of interaction and instruction, each class has an average of 10 to max. 20 students (in-class).
Is the duration of the Bootcamps long enough?
Absolutely. For the Full-Stack and Data Science programs, 12 weeks of intensive practice (40 hours in the classroom with an additional 20-30 for course work per week) will give you what it takes to step into one of these fields.
What coding level do I need?
Though coding experience is not necessarily a prerequisite, we expect you to have been exposed to programming before, whether in industry, academia, or self-study. Motivation, hard-work, and drive are what we're most looking for.
I’d rather participate from another location. Can I attend the program remotely?
Absolutely. For those interested in this option, please select it on the application form.
Is there a difference between the in-person and remote option?
None at all. You’ll be joining the in-class participants for the same program and follow via our live stream platform. You’ll get the same attention from our staff as if you were on site.
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 SIT Academy 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.
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 SIT Academy 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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