Data Science Bootcamp
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. Attend in-person or remote.
Choose your location
Full-Time
12 Weeks
On-site / Remote
English
Program overview
Learn the most in-demand skills
We’ve carefully designed our curriculum to contain the most up-to-date tools currently in-demand on the job market.
Develop your own portfolio
For 3 months, you will have the chance to work on different projects and to showcase your work to potential employers.
Apply what you learned
For the last 4 weeks, you will work together in small teams on your final Capstone project, allowing you to put into practice the key learnings from the teaching weeks. The program ends by presenting your work in front of a large audience on the final evening.
Career support
Your success is our success! We are here to work with you on finding your dream job. We will coach you, share tips and tricks on the job search strategy, give you advice for your next job interview, and connect you with potential hiring companies.
Choose your location
Click on your preferred location to see the corresponding course offering. Simply choose between our on-site locations or opt for a remote course and participate from anywhere in the world.
What you'll learn
We’ve carefully designed our curriculum to contain the most up-to-date tools demanded by the job market. This is what makes our Data Science Bootcamp so innovative and what will enable you to take the next step in your career.
0
Preparation work (1-2 weeks)
- 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
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
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
Karolin Franke
"I had my eyes set on working for a certain company. SIT Academy reached out to them regarding a final project and I’m now a Data Scientist there!"
Before: Pharma Intern
After: Data Scientist at Ava Women
Linda Maria De Cave
"The instructors had amazing backgrounds in famous companies such as Google and LinkedIn, always able to stimulate my interest and to answer my questions."
Before: Post-Doctoral Researcher Mathematics
After: Data Scientist at eBay
Michal Ziemski
"An invaluable and life-changing experience! It never gets boring and keeps satisfying the never-ending appetite for knowledge."
Before: Postdoctoral Researcher Biology
After: AI Engineer at Oto.ai
Application process
Apply to the program
Send us your CV or LinkedIn profile
First motivational interview with SIT Academy
Prepare for the technical interview
Pass the technical interview
Pay a deposit to secure your spot
Complete your preparation work before the Bootcamp starts
FAQs
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