Chemometrics 0600-S2-CM-C

Laboratory program content:
The exercises aim to acquire practical skills enabling the independent use of computer programs in preliminary data processing, modeling relationships, planning experiments, and advanced statistical methods.
In the case of exercises, it is necessary to have the ability to use an MS Excel spreadsheet and knowledge of matrix calculus.

Total student workload

Contact hours with teacher: - participation in exercises – 25 hrs - consultations - 10 hrs Self-study hours: - preparation for exercises – 25 hrs - preparation for test/examination – 15 hrs Altogether: 75 hours: 25 = 3 ECTS

Learning outcomes - knowledge

W1: Knows the methodology, goals, and areas of application of chemometric methods K_W01, K_W04 W2: Knows methods of analysis and control of raw data and their transformation (normalization, standardization) K_W01 W3: Knows various methods of planning and optimizing experiments K_W01 W4: Knows how to create a single- and multi-dimensional chemometric regression model K_W01 W5: Knows examples of the practical application of chemometrics in medical sciences K_W04

Learning outcomes - skills

U1: Can analyze and transform raw data K_U06 U2: Applies in practice selected methods of planning measurements and optimizing experiments K_U01, K_U05 U3: Uses various statistical calculation programs K_U01 U4: Can construct single- and multi-parameter chemometric regression models and check the significance of variables, model adequacy, and prognostic abilities K_U06 U5: Can analyze multidimensional experimental data using selected chemometric methods and correctly interpret the results K_U01, K_U06, K_U07

Learning outcomes - social competencies

K1: Is aware of the level of his knowledge of chemometrics and the need for continuous training and personal development K_K01 K2: Is aware of the role of chemometrics in medical sciences K_K01

Teaching methods

Laboratory: Exploratory teaching methods: Practical, classic problem-solving

Expository teaching methods

- informative (conventional) lecture

Exploratory teaching methods

- practical

Type of course

compulsory course

Prerequisites

MS Excel basic knowledge, mathematics at the secondary school level, and knowledge of matrix calculus.

Course coordinators

Piotr Szczepański

Assessment criteria

Assessment methods:
laboratory - K_W01, K_U01, K_U06, K_U07
Assessment criteria:
Lecture: written examination, test: fail- 0÷49 %, satisfactory- 50÷60%, satisfactory plus- 61÷65%, good - 66÷75%, good plus- 76÷80%, very good- 81÷100%.
Laboratory: two tests fail- 0÷49 %, satisfactory- 50÷60%, satisfactory plus- 61÷65%, good - 66÷75%, good plus- 76÷80%, very good- 81÷100%.

Practical placement

Not applicable

Bibliography

1. Richard G. Brereton, Chemometrics: data analysis for the laboratory and chemical plant, Wiley, 2003.
2. Practical guide to chemometrics, ed. Paul Gemperline, Taylor&Francis, 2006.
3. Andrew G. Mercader, Chemometrics Applications and Research: QSAR in Medicinal Chemistry, Apple Academic Pr, 2021.
4. James Miller, Statistics and Chemometrics for Analytical Chemistry, Pearson Education, 2018.

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 0600-S2-CM-C in USOSweb