Learn about the data science lifecycle;
* Apply Python as a programming language to perform data analysis tasks;
* Become acquainted with the data manipulation process and how to achieve this in Python;
* Get introduced to basic machine learning algorithms and their applications, network science techniques for modeling, analyzing and reasoning about relationships between entities
* Understand and apply data interpretation and visualization tools

PLEASE NOTE THAT THE INFORMATION ABOUT THE TEACHING AND ASSESSMENT METHOD(S) USED IN THIS COURSE IS WITH RESERVATION. A RE-EMERGENCE OF THE CORONAVIRUS AND NEW COUNTERMEASURES BY THE DUTCH GOVERNMENT MIGHT FORCE COORDINATORS TO CHANGE THE TEACHING AND ASSESSMENT METHODS USED. THE MOST UP-TO-DATE INFORMATION ABOUT THE TEACHING/ASSESSMENT METHOD(S) WILL BE AVAILABLE IN THE COURSE SYLLABUS.

Data science is an interdisciplinary field concerning scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured. This course presents the key four aspects of data science: data acquisition and preparation for investigation (scrapping, wrangling, cleaning, sampling, management) to guarantee high quality and quick and reliable access, exploratory data analysis to generate hypotheses and intuition, modelling based on statistical/machine learning and correct communication of the analysis outcomes through visualisation, storytelling and reporting. Lectures and tutorials emphasise the practical use of these aspects and prepare students for developing real-world data-driven applications.

Data Science by John D. Kelleher and Brendan Tierney Data Science from Scratch by Joel Grus Python Data Science Handbook by Jake VanderPlas

BENC1002 Calculus
BENC1004 Linear Algebra