About this course
In our increasingly digitized society, with sensors embedded in our bodies, equipment, and surroundings, we are generating, collecting, and storing data at unprecedented rates. Within this vast sea of data lie insights crucial for understanding, predicting, and impacting every aspect of our existence, including human behavior, financial trends, sustainable development, and health and illness. Extracting these insights requires careful execution at each step in the data analytics pipeline.
In this course, we will take a hands-on approach to explore the key steps in the data analytics pipeline: data gathering, curation, and transformation; the use of computational and statistical tools to analyze both small and large datasets; and data visualization and reporting of analytical insights. Through real-world case scenarios, we will also evaluate and reflect on the validity of the analytical models.
Syllabus
Pre-requisites
One year of computer science, a course in algorithms and data structures. A course in statistics and a course in linear algebra are recommended. Knowledge of at least one programming language (e.g. in Python/Javascript/Java/C++/Matlab).
Aniss Aiman Medbouhi
FacultyPh.D. candidate in Computer Science, KTH Royal Institute of Technology (2022–present). M.Sc. double degree in Computer Science and Engineering/General Engineering, KTH Royal Institute of Technology/Ecole Centrale Marseille (2017–2021). Previous experience developing an algorithm of toxicity risk prediction for anti-cancer chemotherapy and supporting KTH’s Machine Learning and Artificial Intelligence courses. With DIS since 2025.