Course

Python for Natural Sciences and Engineering (MOD905)


Dette er emnebeskrivelsen for studieåret 2025-2026. Merk at det kan komme endringer.

Fakta

Emnekode

MOD905

Vekting (stp)

10

Semester undervisningsstart

Autumn

Undervisningsspråk

English

Antall semestre

1

Vurderingssemester

Autumn

Timeplan

Vis timeplan

Litteratur

Søk etter pensumlitteratur i Leganto

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:

  1. From Basic to Advanced Python Programming: A refresh of Python programming and Deep dive into Python libraries such as NumPy, SciPy, Pandas, and Matplotlib.
  2. Data Analysis and Visualization: Techniques for handling large datasets, statistical analysis, and creating informative visualizations.
  3. Machine Learning in Science and Engineering: Basics of machine learning algorithms and their applications in predictive modeling and data-driven investigation.
  4. 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.

Forkunnskapskrav

Ingen

Anbefalte forkunnskaper

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.

Fagperson(er)

Course teacher:

Aksel Hiorth

Course coordinator:

Nestor Fernando Cardozo Diaz

Course teacher:

Enrico Riccardi

Method of work

Lectures and labs

Åpent for

PhD Candidates

Emneevaluering

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Kontakt

Course teacher:

Aksel Hiorth

Course coordinator:

Nestor Fernando Cardozo Diaz

Course teacher:

Enrico Riccardi

Tilbys av

Faculty of Science and Technology

Department of Energy Resources

The course description is retrieved from FS (Felles studentsystem). Version 1