Python for Natural Sciences and Engineering (MOD905)


Course description for study year 2025-2026. Please note that changes may occur.

See course description and exam/assesment information for this semester (2024-2025)
Facts

Course code

MOD905

Version

1

Credits (ECTS)

10

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Exam semester

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Language of instruction

English

Offered by

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.

Required prerequisite knowledge

None

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

Course teacher(s)

Course teacher:

Enrico Riccardi

Course coordinator:

Nestor Fernando Cardozo Diaz

Course teacher:

Aksel Hiorth

Method of work

Lectures and labs

Open for

PhD Candidates

Admission requirements

 

Course assessment

The faculty decides whether early dialogue will be held in all courses or in selected groups of courses. The aim is to collect student feedback for improvements during the semester. In addition, a digital course evaluation must be conducted at least every three years to gather students’ experiences.

Literature

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