Part-Time CourseData Science & AI Intensive Program
Boost your career with our 22-week part-time Intensive Program and learn new skills in Python, Data Analytics, Machine Learning, Deep Learning, NLP and Generative AI.
Part-Time
weeks
Zurich
English
Program overview
Upcoming Dates
Course dates
Mar 04 - Jul 31
Apply by
Feb 11
Tuition
12'700 CHF
Format
On-site
Course dates
Aug 19 - Jan 23
Apply by
Jul 29
Tuition
12'700 CHF
Format
On-site
Schedule doesn't fit your needs?
Check out our remote options or the Full-Time program.
Looking for financing? Check out our financing options.
Schedule
- Tue
Remote
- 18.00 - 21.00Lecture
- Thu
Remote
- 18.00 - 21.00Practice
- Sat *
On-site
- 09.00 - 12.00Lecture
- 13.00 - 16.00Practice
* The course takes place every second Saturday.
LectureLearn from our instructors who are experts in their respective fields and get introduced to new topics during live lectures.
PracticeWork 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.
Thi Tuyen Nguyen
❝The intense curriculum of the bootcamp pushed me outside my comfort zone, enhancing my resilience and passion for continuous learning while equipping me with essential skills for a transformative career in data science.❞
Before: Postdoctoral Researcher
After: Artificial Intelligence Intern at Baader Bank AG
What you will learn
After applying
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 Discord to support you.
Week before start
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.Week 1 - 3
Data Science toolkit
- 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.
Week 4 - 5
Statistics & experimental design
- 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.
Week 6 - 7
Data visualization
- 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.
Week 8 - 11
Classical & advanced Machine Learning (ML)
- 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).
Week 12 - 14
Deep Learning
- 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.
Week 15 - 17
Natural Language Processing (NLP)
- 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 features and train classical ML models.
- Solve diverse problems like classification, recommendation, summarization, named entity recognition, and more.
- Use Deep Learning models and Transfer Learning including Transformers to solve more complex tasks (language translation, contextual similarity, semantic search, and more).
- Learn about Generative AI for NLP, Prompt Engineering and Large Language Models (LLMs) like ChatGPT to solve diverse NLP tasks including QA Chatbots.
Week 18
Machine Learning Engineering
- 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.
Week 19 - 22
Capstone project
- 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.
Application process
Apply to the program hereSend us your CV or LinkedIn profile
First motivational interview with Constructor Academy
Prepare for the technical interview
Pass the technical interview
Pay a deposit to secure your spot
Complete your preparation work before the Intensive program starts
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 Intensive program.
Estimated time to complete: 15 hours
Topics
Data Analytics
Examine large and complex data sets to uncover insights, trends, and patterns that can inform decision-making.
ML & AI
Train computer algorithms to learn patterns and make predictions or decisions without explicit instructions, based on data inputs.
LangChainPython
Python is taking over the world!
Python is the market leader in many sectors:
- Data Analytics
- Machine Learning
- Artificial Intelligence
- Scientific Research
- Software Prototyping
- Generative AI
- And more...
Hands-on
Over 360 hours of hands-on training
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.
InstructorsOur capstone projects
What clearly sets us apart from other courses 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
Finish your professional transformation by working on an industry relevant capstone project.
Preparation phase
Organize your project
- Receive and/or set the requirements
- Set milestones
Development/Creation phase
Work in a team
- Use collaborative tools
- Split and coordinate different tasks
- Learn from your fellow teammates
- Build your first real world project
Presentation
Leave your first mark in the industry
Present your capstone project with your team mates in front of attendees from our network.
There is no upcoming final projects date fixed yet. Sign up to our newsletter if you want to get notified whenever the next date gets published.
One step ahead: Detecting unusual human motions
Project by:
Alaa Elshorbagy, Vincent von Zitzewitz, and Jonas Voßemer
Choose your location
Visit our campus in Zurich
Would you like to see what your time at Constructor Academy could be like and where our students spend most of their time? Then contact us for a visit of our campus.
Constructor Academy
Lintheschergasse 7
8001 Zürich
+41 (0)44 797 51 50
Financing options
At Constructor Academy, we believe that finances should never be a barrier to accessing the education and training that can help individuals achieve their goals. That's why we offer a variety of financing options to make our courses more accessible to a diverse range of students. We also work with external organizations that provide financial assistance to those in need.
Certificate from top coding school
Get certified by Constructor Academy, one of the world's top coding academies. Share your certificate on social networks, CVs and more. Boost your career with the new skills that you gained.
Upcoming events
Attend one of our events. Discover our upcoming workshops, info sessions, final presentations and webinars on trending topics.
Break into tech | Info session
08. Jan 25, 06:00 PM - 07:00 PM GMT+1 
Online via ZoomJoin us on January 8, from 6 p.m. to 7 p.m. for an engaging info session about Constructor Academy. We will provide an overview of our Data Science and Full-Stack intensive programs, along with brief introduction to our short courses, including Python and Mastering Generative AI. You'll gain insights into our curriculum, career outcomes, and the admissions process. Don't miss this opportunity to learn more about our programs and take the first step toward a fulfilling new career in technology. Register here to secure your spot in the info session. We look forward to seeing you soon!
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.
What's the course schedule for the part-time program?
The part-time program spans 22 weeks, featuring engaging lectures every Tuesday and Thursday from 6 PM to 9 PM, as well as every other Saturday. Beyond class hours, students dedicate additional time to reinforce their learning and apply their skills through hands-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 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.Which job positions can I apply for after the program?
Completing our Data Science Program opens up numerous possibilities in the tech job market. Here are some of the job positions that you can apply for as a graduate: • Data Scientist • Data Analyst • Data Engineer • Data Architect • Machine Learning Engineer • Business Intelligence Engineer
When do I have to pay the tuition fee for the part-time programs?
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.
Will I receive a certificate upon successfully completing the course/program?
Yes, upon successfully completing the course or program, you will receive both a digital and a printed certificate from Constructor Academy. These certificates serve as official recognition of your achievement and can be used to enhance your resume, LinkedIn profile, or portfolio.
Instructors
Marcus Lindberg
Data Science Part-time Program Manager
Starting his career in clinical immunotherapy research, Marcus was exposed to the pressing need for better ways to make sense of patient data. With a growing interest in personalized therapies, he pursued a MSc in Bioinformatics at the University of Edinburgh and joined ETH Zürich’s Clinical Bioinformatics Unit. Now at SIT Learning, he is able to keep refining his analytical toolbox while helping people reach their goals along the way.
Dr. Mark Rowan
Instructor
What drives you? For me, it's about using data to tell a story and change the world. Whether it's neuroscience, aerospace, telecoms, insurance, or voice tech - I love getting into the data and making things happen.
Gerry Liaropoulos
Instructor
As an experienced Data Scientist in the fascinating sector of Life Sciences, I am using a variety of Machine-Learning methods to help the industry make more informed decisions with the end goal of effecting a positive change on patients’ lives.
Patrick Senti
Freelance Analytics Consultant
Patrick has been building analytics solutions since 1995, applying machine learning, data engineering, data analytics & visualization. Helping customers in the finance, transportation and retail industries his experience includes software engineering & architecture in distributed systems from enterprise backends to mobile & IoT systems. Senior BI/Data Science & Software Engineer since 1995 * Applied data science, data engineering, software engineering, big data * Wide industry experience in Finance, retail, logistics Roles * Data scientist/data & ML engineer, software engineering, consulting * Lead Data Analytics Practice at swissQuant * Senior Software Engineering, Tech Lead at Credit Suisse, Logicalis, SAS, IBM Education * CAS ETH Zürich in Computer Science & Distributed Systems * Swiss Dipl. Business Informatics (Professional Master) * Executive MBA Freelance Analytics Consultant, patrick@productaize.io Founder omegaml.io Helping companies to productize and operationalize ML
Dr. Ekaterina Butyugina
Data Science Program Manager & Instructor
Ekaterina studied mathematics at university and worked as Junior Researcher in Russia where she did her PhD in Continuum Mechanics. Looking for the opportunity to find something close to science but more dynamic and applicable to real life, she joined the Data Science program, then stayed on as a TA and later joined the team as a Data Science Consultant. She likes to work with data and apply both analytical and creative approaches, trying new techniques and sharing them with other people.
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