Curriculum
The program consists of 30 hours of instruction, combining theoretical lectures, practical workshops, and project work.
Introduction
1h Lecture + 3h ExercisesIntroduction to Python
Basic syntax, installation of tools, and library setup.
Data Basics
1h Lecture + 4h ExercisesPython for Data Processing
Variables, control structures, functions. Deep dive into NumPy and pandas.
Automation
1h Lecture + 4h ExercisesAutomation & Advanced Processing
Task automation scripts. Cleaning, normalization, transformation, and loading of scientific data.
AI Fundamentals
1h Lecture + 3h ExercisesIntro to AI & Deep Learning
Theoretical foundations, overview of algorithms, and working with PyTorch.
Neural Networks
1h Lecture + 4h ExercisesDeep Neural Networks Application
Building and training models for classification and regression.
Visualization
1h Lecture + 3h ExercisesDimensionality Reduction
PCA, UMAP, and visualization of high-dimensional data.
Final Project
3h SeminarProject Work
Application of learned techniques to real-world bioscience problems.