Data Science Foundations
This course offers a beginner-friendly introduction to programming and data science, tailored for students without prior coding experience.
Overview
This course, Co-developed with Prof. Ziawasch Abedjan, introduces programming and data science for students with no coding background. Using a problem-driven approach, it teaches how to analyze data, test hypotheses, and distinguish correlation from causation. Students will work in browser-based Jupyter Notebooks to explore real-world questions and build coding skills. Topics include data visualization, random variables, hypothesis testing, prediction, and ethics. By the end, students can write simple programs and critically engage with data.
Which topics will be covered?
- Data visualization and exploratory analysis
- Statistical inference and hypothesis testing
- Ethical considerations in data analysis
- Basics of Deep Learning
What will I achieve?
On completion of the course you will be able to…
- Write basic Python programs to process and analyze data
- Visualize and interpret patterns in structured datasets
- Formulate, test, and evaluate data-driven hypotheses
- Understand key statistical concepts and their real-world applications
- Critically assess ethical implications in data analysis
Which prerequisites do I need to fulfill?
The course is designed for beginners from all academic backgrounds with basic math understanding (in particular, stochastic).
