AI Data Science Programming for Advanced Users (Hybrid)
excl.
Training program facts
Key learnings
The goal is to give advanced learners the opportunity to design data science solutions with Python.
optimize and implement by using advanced analytical methods,
machine learning and scalable data processing techniques to complex,
real problems.
Requirements
• Very good knowledge of Python
• Practical experience with Pandas, NumPy, and data analysis
• Basic knowledge of machine learning
• Experience with Jupyter Notebooks and Git (recommended)
Program content
| 1 week | Module | Method | Remark / Aids |
| Mon | (9:00) Data Normalization
(10:00) Exercises in data normalization (11:00) 1 hour (coding together) Big Data Normalization and model preparation |
Frontal & Brainstorming
Self-organized learning Frontal |
PP Presentation
|
| (13:00) Imputation and Data Cleaning
(2:30 p.m.) Exercises in data cleaning: how fast can you clean your dataset? (3:30 p.m.) 1 hour (coding together) tutorial: Working with massive datasets (4:30 p.m.) Independent work on projects |
Frontal & Brainstorming
Plenum & Trainer shows how to use Python Environment in Git.
Group work, Discussion and subsequent solution in plenary Individual work (individual) As homework |
Github account and open source programming
Individual work. Afterwards, the trainer will evaluate and analyze the frontal.
Connection to the training server must be through RDP (Remote Desktop Protocol). Technical requirement on the training server: PyChar (as open source full featured IDE ). It must be downloaded and installed locally on every student laptop. |
|
| Tue | (9:00) Multicollinearity
(10:00) 30 min exercises and 30 min solutions on solving multicollinearity (11:00) 1 hour (coding together) implementing automated checks |
Frontal Individual work Group work |
Connection to the training server must be through RDP
(Remote Desktop Protocol). |
| (13:00) Advanced data transformations for addressing multicollinearity
(2:30 p.m.) Exercises in multicollinearity: when to transform? (3:30 p.m.) 1 hour (coding together) advanced data imputation (16:30) 1.5 hours Project Work & Questions |
Frontal
Group work
Group work
Individual work |
PP Presentation
Github account and open source programming through Git repository. (Local Desc Drive & SSH Key for Remote) -For all afternoon-
|
|
| Wed | (9:00) Dimensionality Reduction
(10:00) Exercises in PCA, kPCA (11:00) 1 hour (coding together) tutorial in Python – Reducing data dimension, and making predictions. |
Frontal
Plenum & Trainer shows how to create SQL queries in Git.
Group work
|
All students install Postgre DB on their laptops |
| (13:00) 1-hour quiz to recap major topics in statistics
(2:00 p.m.) 1.5-hour lecture on heteroscedasticity and ANOVA (3:30 p.m.) 1 hour (coding together) tutorial on analysis of variance in Python (16:30) 1.5 hours Project Work & Questions
|
Frontal
Individual work & support from the trainer
Group work |
All students install SQLAlchemy/Alembic on their laptops. | |
| Thur | (9:00) Introduction to Object Relational Databases
(10:00) 30 min exercises on Database Queries & 30 min solutions & explanation (11:00) 1 hour (coding together) Setting up a Postgres Database
|
Frontal
Individual work & support from the trainer Group work
|
Cheat-Sheet for python modules used in Boot Camp |
| (13:00) 1.5-hour lecture on Postgres + Pandas and SQLAlchemy
(14:30) 30 min exercises on Postgres Operations and SQLAlchemy & 30 min solutions and explanations (3:30 p.m.) 1 hour (coding together) tutorial on Pandas + SQLAlchemy (16:30) 1.5 hours Home exercises and coding practice. |
Frontal
Individual work & support from the trainer
Group work
|
||
| Fri | (9:00) Lecture: Bit Data – how to deal with super massive datasets?
(10:00) Exercises and Solutions for manipulation of big data (11:00) 1 hour (coding together) tutorial on Big Data handling |
Frontal
Individual work & support from the trainer
Group work |
Pydantic is a Python package that can offer simple data validation and manipulation. It must be downloaded and installed on Students Laptops. |
| (13:00) 1.5 hour lecture on time series data: How is it different?
(2:30 p.m.) 1-hour exercises on manipulation of dependent datasets (3:30 p.m.) 1 hour (coding together) tutorial on time series datasets in Python (pandas) (16:30) 1.5 hours Project Work & Questions |
Frontal
Individual work & support from the trainer
Group work |
Redis is an in-memory key-value pair database typically classified as a NoSQL database.
It must be downloaded and installed on Students Laptops.
|
| 2 week | Module | Method | Remark / Aids |
| Mon | (9:00) Lecture: Non-linear regression for classification
(10:00) Exercises in Scikit-learn (11:00) 30 min solutions and explanations |
Frontal
Individual work & support from the trainer
Group work
|
Visual Studio Code (incl. Javascript) must be downloaded and installed on the students' laptops beforehand. Internet browser must also be available there. Internet connection too. |
| (13:00) Lecture on Assessing Classification Accuracy
(2:30 p.m.) 30 min exercises + 30 min solutions Classification Accuracy (Predicted vs. Observed, ROC, MSE) (3:30 p.m.) 1 hour (coding together) tutorial: Setting up a classification pipeline (4:30 p.m.) 1.5 hours of project work and questions |
Frontal
Individual work & support from the trainer
Group work
Group work & quiz |
||
| Tue | (9:00) Introduction to Classification Trees
(10:00) Exercises in using classifiers (11:00) Classification Tree Tutorial — voting on Thursday's topic — |
Frontal Individual work & support from the trainer
Group work
|
Browser and Internet must be available.
Connection to the training server must be available in advance |
| (13:00) 1.5-hour lecture on Random Forest
(2:30 p.m.) 30 min exercises + 30 min solutions in using the Random Forest Algorithm (Machine Learning) (3:30 p.m.) 1 hour (coding together) tutorial: Preparing data for Random Forest (4:30 p.m.) Project work and questions |
Frontal
Individual work & support from the trainer
Group work
Group work & quiz |
Cryptools like Whireshark or cleopatra. | |
| Wed | (9:00) Machine Learning Lecture (SVM)
(10:00) Exercises in Scikit-learn (11:00) 30 min solutions and explanations |
Frontal
Individual work & support from the trainer
Group work |
Connection to the training server must be available in advance |
| (13:00) 1.5-hour lecture on bagging and ensemble methods
(2:30 p.m.) 30 min exercises + 30 min solutions on Machine Learning: how to use machine learning methods? (3:30 p.m.) 1 hour (coding together) tutorial: Setting up a machine learning model (4:30 p.m.) 1.5 hours of project work and questions |
Frontal
Individual work & support from the trainer Group work
Group work & quiz |
||
| Thur | (9:00) Free Lecture (on voted topic)
(10:00) 30 min exercises and 30 min solutions on Forecasting (11:00) 1 hour (coding together) Setting up a forecasting pipeline |
Frontal
Individual work & support from the trainer Group work |
Flask as a web framework written in Python Must be installed. |
| (13:00) Data Structures and Algorithms
(2:30 p.m.) 1 hour data structures & algorithms (3:30 p.m.) 1 hour (coding together) tutorial on Data Structures and Algorithms (Technical Interview Prep) (16:30) 1.5 hours Project Work & Questions |
Frontal
Individual work & support from the trainer Group work Group work & quiz
|
Flask as a web framework written in Python
Must be installed. |
|
| Fri | (9:00Common Technical Interview Questions (algorithms walkthrough)
(11:00) Common project management frameworks (SCRUM / Agile) |
Frontal
Individual work & support from the trainer
Group work |
Cryptools like Whireshark or cleopatra. |
| (13:00) Technical Interview Drilling (all bootcamp participants practice job interviews + reviewing interviews)
(16:30) 1.5 hours Project Work & Questions |
Frontal
Individual work & support from the trainer
Group work
Group work & quiz
|
Browser and
Cryptools like Whireshark or cleopatra. |
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AI Data Science Programming for Advanced Users (Hybrid)
01.06.2026
12.06.2026
excl.
Zurich Airport