Data Analysis Fundamentals
At a glance
Certificate of attendance "Data Analysis Fundamentals" (2 ECTS)
01.03.2023 17:30, 11.10.2023 17:30
6 evenings x 2.5 hours or 5 evenings x 3 hours
There is also the option for students to join online as well.
Language of instruction:
01.03.2023 17:30 - 20:00
08.03.2023 17:30 - 20:00
15.03.2023 17:30 - 20:00
22.03.2023 17:30 - 20:00
29.03.2023 17:30 - 20:00
05.04.2023 17:30 - 20:00
11.10.2023 17:30 - 20:30
25.10.2023 17:30 - 20:30
01.11.2023 17:30 - 20:30
08.11.2023 17:30 - 20:30
15.11.2023 17:30 - 20:30
Objectives and content
To develop models using machine learning and neural networks, data scientists first need to acquire data, explore data, evaluate data quality then perform data transformation as a pre-requisite to learn from their dataset and produce models from the most basic linear regression models to advanced deep learning models. Without a solid understanding of data and data quality, scientists are unable to develop models or worst, they will produce invalid predictive analysis. This course focuses on the first steps in the data pipeline and provides the necessary foundation to progress in their data science journey.
Are you the "Excel" go-to person in your office, are you a "free style" data analyst? Do you like to use data to make decisions? Do you like to create infographics such as line plots, bar charts, box-plots, swarm-plots, etc.? This course is aimed at professionals who want to develop their careers towards data science. You will take your first steps using the programming language Python and you will learn by doing. You will get plenty of hands-on practice with weekly guided data analysis sessions and homework assignments. The course is taught in English. No prior Python programming required.
After completing the module, students will be able to:
- Understand, select when and how to use the main Python libraries for data analysis (NumPy, Pandas)
- Display an advanced understanding of data acquisition
- Explore a dataset and apply data quality techniques
- Perform data transformation for machine learning
- Produce statistical data analysis
- Create data visualizations
- Reflect on the characteristics and suitability of a dataset for ML
The module covers the following topics:
- Getting started with Python and Jupyter notebook
- Introduction to Numpy & Pandas
- Data exploration
- Data cleansing
- Data transformation for ML
- Working with databases
- Visualization with Matplotlib and Seaborn
There are 6 lessons organized once a week on Thursday Afternoon. After 5 lessons the students work on their final project for about two weeks. The final project presentation will be held during the last lesson.
CAS in Digital Life Sciences
This module is part of the CAS in Digital Life Sciences continuing education programme, but can also be attended independently of the CAS. Credit points earned for this module can be credited to the CAS course at a later date, provided the relevant general conditions are fulfilled.
More information here: CAS in Digital Life Sciences
The module will consist of lectures and practical exercises. In addition to lectures, students will be required to self-study selected topics. Students will work in groups on a project and present their results at the end of the course.
- Exercises during the course: 50%
- Project: 50%
Enquiries and contact
Nicolas Vu Huu has 20+ years of international experience in the corporate world and has held executive positions in global financial institutions (Deutsche Bank, Morgan Stanley, Julius Baer, Vontobel) as well as medical devices and implants for hearing care (Sonova Group). He has served in the roles of Head of Engineering then COO at Infonic AG and is currently Head of People Analytics at Bank Vontobel. Nicolas Vu Huu has a pluri-disciplinary education background BSc. Maths & Physics (France), MSc. Computer Science (Polytech, France), Adv. Studies in Finance (NYU, USA), BA in Literature and Sociolinguistics (France) and Adv. Studies in Applied Data Science: Machine Learning (EPFL, Switzerland).
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