Course

Data analytics and visualization in Python with subsurface applications (E-MOD322)

One of the four-micro-course program created for professionals who need to incorporate Python programming in their daily work, but who have little or no background in Python.


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

Facts

Course code

E-MOD322

Credits (ECTS)

2

Semester tution start

Spring

Language of instruction

English

Number of semesters

1

Exam semester

Spring

Time table

View course schedule

Literature

Search for literature in Leganto

Content

This micro-course is the second in a four-micro-course program created for professionals who need to incorporate python programming in their daily work, but who have little or no background in Python. At the end of this course, students will have the basic skills to fetch, clean, visualise and analyse subsurface data using Python. Students will have the opportunity to work with different domain-specific data types (e.g. LAS, SEG-Y, xyz).

Learning outcome

At the end of the course, students can:

i Create plots, tables, and graphs using Python.

ii Import Python libraries for data analysis and visualization, such as NumPy, Pandas,

Matplotlib, and Seaborn.

iii Visualize and analyze orientation data, grids, and images using Python.

iv Visualize and analyze domain-specific data using external libraries (e.g. Lasio, Segyio, PyVisA).

v Acquire and use the technical vocabulary for data analysis and visualisation.

Required prerequisite knowledge

None

Recommended prerequisites

All previous micro-courses (none for module 1).

Exam

Form of assessment Weight Duration Marks Aid
Home exam 1/1 2 Weeks Passed / Not Passed All


The assignment is a practical coding project. The students will submit the code with internal documentation and the output with a short explanation.

Re-submission of project follows the next offering of the course; re-enrollment is not required.

Course teacher(s)

Course teacher:

Aksel Hiorth

Course teacher:

Nestor Fernando Cardozo Diaz

Course coordinator:

Enrico Riccardi

Method of work

a This is a project-based course where the students learn concepts and their application to practical problems. We use multiple forms of dissemination: the student learns from teaching material prior to the class. In the class, physical teaching is mixed with guided coding activities during 2 days. After the class, virtual meetings are held to discuss the student coding project.

b Teaching material available prior to the course meeting

c 2 days physical teaching with integrated lectures and labs

d Python programming on laptop/computer during meetings and at home

e One virtual meeting during the micro-course to assist with the project

f Virtual meeting at the end of the micro-course to discuss the project outcomes

Open for

Course assessment

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.

Contact

Course teacher:

Aksel Hiorth

Course teacher:

Nestor Fernando Cardozo Diaz

Course coordinator:

Enrico Riccardi

Offered by

Faculty of Science and Technology

Department of Energy Resources

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