Technologies of data analysis in natural sciences
Course:
Structural unit: Institute of High Technologies
Title
Technologies of data analysis in natural sciences
Code
ОК.14
Module type
Обов’язкова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2023/2024
Semester/trimester when the component is delivered
4 Semester
Number of ECTS credits allocated
3
Learning outcomes
Ability to use the laws, theories and concepts of chemistry in conjunction with relevant mathematical ones tools for describing natural phenomena. The ability to build adequate models of chemical phenomena, investigate them to obtain new conclusions and deepening the understanding of nature, including the use of molecular, mathematical and computer modeling. Ability to apply computer modeling methods to solve scientific, chemical-technological problems and problems of chemical materials science.
Form of study
Prerequisites and co-requisites
1. Possession of scientific-theoretical and practical educational material
disciplines that are taught to students of the "Bachelor" educational level.
2. Knowledge of basic elementary methods of mathematical statistics and
programming.
3. Mastering the skills of elementary operations with real numbers and
real variables.
Course content
In the course, the set is studied and systematized
modern methods of statistical and analytical data processing that allow
evaluate the received data, process it, identify features in the data,
and interpretation of the results of interdisciplinary research. The course includes
self-examples of the application of the proposed research methods in
natural sciences. Detailed methods of visual representation are given
data using Python programming language libraries.
Recommended or required reading and other learning resources/tools
1. J. Walkenbach. Excel 2013 Formulas. Wiley, 2018.
2. O. M. Vasiliev, Programming in the Python language. Ternopil: Bohdan, 2019.
Internet resources:
1. Origin user guide, OriginLab Corp. 2020.
https://d2mvzyuse3lwjc.cloudfront.net/pdfs/Origin2020b_Documentation/English/Ori
gin_User_Guide_2020b_E.pdf#zoom=100
2. UCI Machine Learning Repository
https://archive.ics.uci.edu/ml/index.php
Planned learning activities and teaching methods
lectures, practical
Assessment methods and criteria
- semester assessment:
1. Modular control work 1 – RN 1.1; 1.2. – 15 points/ 20 points
2. Modular control work 2 - RN 1.3; 1.4 – 15 points/ 20 points
3. Evaluating the essay RN 2.1 – 30 points
- final evaluation: in the form of an exam - 40 points
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Departments
The following departments are involved in teaching the above discipline