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Python in the Laboratory

In preparation: a course on using Python for experimental data acquisition, processing, numerical analysis, visualization and image-based workflows.

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Status

The course is in preparation. At this stage the page is informational: it describes the planned scope, audience and working format. Teaching materials, notebooks and assignments will be added later.

About the course

Python in the Laboratory is a course on practical Python workflows for experimental data. The main focus is the path from raw measurement files to structured numerical analysis, visualization and lightweight image-processing procedures.

The course treats Python as a research tool: for automating repetitive work, extracting data, checking numerical results, preparing plots and building reproducible analysis workflows.

Audience

The course has two organizational variants:

  • MSc track: second-cycle physics students, year 1 or 2.
  • Doctoral track: doctoral students using data analysis, computational tools and visualization in research work.

The expected background includes basic programming in any language, elementary statistics and mathematical analysis, and readiness to work independently with scientific-library documentation.

Scope

The planned scope includes:

  • Python for research and laboratory workflows
  • code quality practices: readability, modularization, exception handling and debugging
  • data organization, file-system workflows and extraction from text, binary, CSV and JSON formats
  • linear algebra, multidimensional arrays and fitting models to experimental data
  • scientific plots and data visualization
  • digital images as measurement matrices
  • basic image-processing techniques: filtering, morphology, segmentation and edge detection
  • selected machine-learning methods for object detection and classification in visual material

Work format and assessment

The course is planned as a specialist winter-term lecture, 30 hours, 3 ECTS.

Assessment is planned around problem-based assignments. Students will prepare scripts that solve concrete data-analysis or computer-vision tasks using measurement files or images provided during the course.

Literature

Core sources:

  • J. VanderPlas, Python Data Science Handbook, O’Reilly Media.
  • A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, O’Reilly Media.
  • A. Sweigart, Automate the Boring Stuff with Python, No Starch Press.
  • J. M. Stewart, Python for Scientists, Cambridge University Press.
  • official documentation of the discussed libraries.

Last updated

2026-05-05