Data Science for Urban Mobility
- Lectures: Monday 12:15, Delta Center - Narva mnt 18, r-2010.
- Practicals: Wednesday 14:15, Delta Center - Narva mnt 18, r-2034.
- Lecturer: Amnir Hadachi (hadachi@ut.ee)
- Lab Assistant: Shan Wu (Shan.wu@ut.ee), Alireza Akhavi Zadegan
- This semester, the course will be in a hybrid format for the lab session, lecturers, and all the sessions will be recorded.
- You will receive an email before each session with details.
- Lectures:
- Monday at 12:15,
More details about the lectures are in the lectures section.
- Practicals:
- Wednesday at 14:15,
We suggest you check the lab material before you come to the sessions to take advantage of the lab session.
Objective
The rapid development of Data Science and especially the consulting engagement at a high level has made the job market in need of such skilled professionals in the transportation field. This course will help students gain knowledge about nowadays problems in urban mobility and how to use Data Science skills to adequately convey the appropriate questions, understand the limitation, and find suitable solutions.
For this reason, the course is designed to help students to understand the relevance and limitations of data-centric approaches when applied to urban transport in contrast to other methods, such as modeling and forecasting. Furthermore, the course will provide the students with the knowledge required to use Data Science to solve Urban Mobility problems, covering all the traditional steps from data to insight discovery.
Topics
This class will include but not be limited to the following topics:
- Introduction Data Science, Big data, and Python
- Data Preparation
- Spatial Data
- Bayesian Inference
- Machine Learning
- Spatial Analysis
- Data Visualization
- Professional issues and ethics
Discussion Board:
- Critical messages will be announced personally using ÕIS
- Course general discussion will happen on the Course forum channel in Teams.
- Access granted during the first week