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Applied Scientific Data Handling, 4 credits

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This course syllabus was confirmed by The Research Programmes Board at the Faculty of Science 12 December 2022. The course is in the third cycle and amounts to 4 credits.
The course syllabus is formally approved in Swedish. This is a translation.

Learning outcomes

This course develops the basic skills for applied scientific data treatment with a focus on the chemical and physical sciences. The course contains theoretical as well as applied parts to enable the students to perform data analysis in their respective subject with a widely used and freely available computing language (Python).

On completion of the course, participants shall be able to:

Knowledge and understanding

Explain what data are and how data can be described and summarized by statistical methods.
Explain how to formulate and optimize mathematical models to describe data.
Explain how artificial intelligence can be used to describe and categorize image information.

Competence and skills

Create a wide variety of plots, of publication quality, for visualization of data.
Calculate and describe statistical correlations in datasets and evaluate their significance with standard models.
Import and export data from text files and communicate with instrument interfaces for automated data collection.
Describe a dataset with principal component analysis, model functions, and models with optimized sets of parameters.
Read, adapt and transform image information.
Define, adapt and use simple categorizing functions based on artificial intelligence (AI).

Course content

The course is focused on the practical skills necessary to analyze data in chemical sciences. Each topic will be introduced with a short theoretical background followed by a practical tutorial introducing the key concepts and methods on tutorial data and finally on the student’s own dataset. The course deals with the following concepts:

Introduction to Python and data handling.
Importing, cleaning, and plotting data.
Statistical description and evaluation of data and data correlations.
Creation of advanced graphs in publication quality.
Creation of simple functions e.g. cost functions for optimization tasks.
Introduction to singular component analysis (SVD, PCA), model formulations, and optimization routines.
Introduction to image handling and manipulation using self-defined and pre-defined libraries.
Introduction to artificial intelligence for categorization of image information.
Introduction to communication with instruments.

Forms of instructions

The course consists of short introductory lectures and supervised and unsupervised laboratory work using Jupyter Notebooks.

Forms of examination

The assessment is based on a written assignment and an oral presentation.

Grades

Possible grades are Pass and Fail. To pass the course, the student must have passed the written assignment and the oral presentation.s.

Language of instruction

The course is given in English.

Additional Information

The courses NAKE014 and NKE017F cannot be credited together since they overlap much in their content.

Course responsible teacher and unit/division

Jens Uhlig
Email: jens.uhlig@chemphys.lu.se

Division of Chemical Physics, KILU

Do you have questions? Please contact FUstudierektor@kilu.lu.se