Instructor information
Module leader: Prof. Natalia Isaenko
Course information
ECTS: 6 credits
Status: Elective
Semester: 1
Hours: 30/18 (lectures/exercises)
Wednesday 12.00 - 14.00 (Room 47)
Friday 16.00 - 19.00 (Room 47)
Google Classroom: gkouyhvo
Google Meet: https://meet.google.com/whk-ayxw-xqk
Objectives
The programming course for transportation engineering introduces students to fundamental programming concepts essential for analyzing transportation data and solving real-world problems. Beginning with basic algorithms and control flow operators, students progress to advanced topics such as data analysis, simulation methods, and optimization algorithms. Through hands-on exercises, students learn to manipulate transportation data, plot graphs, and apply scientific computing techniques using Python. This synthesis of programming skills with transportation engineering principles equips students with practical tools to address complex challenges in transportation engineering.
Syllabus outline
Part 1: Foundations of Python Programming
Introduction to the Course
Algorithms and Control Flow Operators
Functions
Data Structures in Python
Basic Algorithms examples
Monte Carlo Methods
Simulating a queuing process
Part 2: Data Analysis with Python
Scientific Computing with NumPy
Data Analysis with Pandas
Part 3: Advanced Algorithms and Data Analysis Techniques
Gradient Descent Algorithm
Clustering Algorithms
Simulated Annealing Algorithm
Spatial Data Analysis
Essential reading list
- Lecture notes provided by the instructor