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:

1. Getting started with Python and Jupyter notebook
2. Introduction to Numpy & Pandas
3. Data exploration
4. Data cleansing
5. Data transformation for ML
6. Working with databases
7. Visualization with Matplotlib and Seaborn

There are 5 lessons organized once a week. After 4 lessons the students work on their final project. The final project presentation will be held during the last lesson.

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

You can find an overview of our continuing education programmes in the field of computational science and artificial intelligence here.

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%