Python for Natural Sciences and Engineering (MOD905)
Course description for study year 2025-2026. Please note that changes may occur.
Course code
MOD905
Version
1
Credits (ECTS)
10
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+Exam semester
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+Language of instruction
English
Content
This course is designed for PhD students in natural sciences and engineering who wish to improve their skills in Python programming. The course will cover topics with a focus on real-world applications in scientific research and engineering problem-solving. Emphasis will be on data analysis, simulations, visualization techniques, and artificial intelligence (AI) applications.
Course content:
From Basic to Advanced Python Programming: A refresh of Python programming and Deep dive into Python libraries such as NumPy, SciPy, Pandas, and Matplotlib.
Data Analysis and Visualization: Techniques for handling large datasets, statistical analysis, and creating informative visualizations.
Machine Learning in Science and Engineering: Basics of machine learning algorithms and their applications in predictive modeling and data-driven investigation.
Project Work: Students will undertake a project that applies Python to a specific problem in their field of study.
Learning outcome
Upon completing the course students will:
Have knowledge of Python programming for scientific computing.
Be able to code with Python for data analysis, visualization, and interpretation.
Be capable of automatizing workflows in their respective fields of research.
Understand the role of machine learning and AI in scientific advancements.
Required prerequisite knowledge
Recommended prerequisites
None but knowledge of basic Python is an advantage.
Exam
Form of assessment | Weight | Duration | Marks | Aid |
---|---|---|---|---|
Project Assignment | 1/1 | 12 Weeks | Passed / Not Passed | All |
Individual project connected to the student’s research area and PhD.
Course teacher(s)
Course teacher:
Enrico RiccardiCourse coordinator:
Nestor Fernando Cardozo DiazCourse teacher:
Aksel HiorthMethod of work
Lectures and labs
Open for
PhD Candidates
Admission requirements