Specialized programming of automated systems
Course: Software Engineering
Structural unit: Faculty of information Technology
Title
Specialized programming of automated systems
Code
ОК 33
Module type
Обов’язкова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2021/2022
Semester/trimester when the component is delivered
6 Semester
Number of ECTS credits allocated
5
Learning outcomes
PR-6. Know and be able to use methods and tools for gathering, formulating and analyzing software requirements.
PR-12. It is motivated to choose programming languages to solve the tasks of creating and maintaining software.
PR-22. Analyze, evaluate and select instrumental and computational tools, technologies, algorithmic and software solutions for solving software engineering tasks.
PR-26. Know and be able to apply software verification and validation methods.
Form of study
Full-time form
Prerequisites and co-requisites
1. Know the theoretical foundations of the basic principles of algorithms and data structures, object- oriented program designs, software requirements analysis, software architecture and design.
2. To be able to use a systematic approach to building programs of various complexity.
3. Possess elementary skills of searching and processing Internet information and skills of working in groups
Course content
The discipline "Specialized programming of automated systems" is aimed at achieving competencies in terms of the ability to abstract thinking, analysis and synthesis, the application of general scientific and fundamental knowledge, the skills of using information and communication technologies in order to search, process and analyze information from various sources, the application of fundamental and interdisciplinary knowledge for successfully solving software engineering problems and finding methods and approaches to their solution.
Recommended or required reading and other learning resources/tools
IoT Fundamentals: Big Data & Analytics // Електронний ресурс. Режим доступу: https://www.netacad.com/courses/iot/big-data-analytics
1. Рandas // Електронний ресурс. Режим доступу: https://www.w3schools.com/python/pandas/pandas_intro.asp
2. Python Data Science Handbook | Python Data Science Handbook (jakevdp.github.io)
3. Andreas Mueller & Sarah Guido: Introduction to Machine Learning with Python: A 4. Guide for Data Scientists ; Publisher, O'Reilly Media; 1st edition (November 15, 2016) .
Planned learning activities and teaching methods
Lectures, practical activities, individual work
Assessment methods and criteria
The level of achievement of all planned learning outcomes is determined by the results of the defense of practical work and individual tasks of independent work. Semester assessment of students is carried out during the semester for all types of work. The total score is formed as a weighted sum of points earned by the student for various types of work.
The maximum number of points that a student can receive for work in a semester does not exceed 100 points. The form of the final evaluation is the test. The assessment is carried out by issuing a final grade, which is defined as the sum of points for all successfully assessed learning outcomes. To receive credit, it is mandatory to complete all practical work (minimum grade - 25 points, maximum - 49 points), testing work (minimum grade - 6 points, maximum - 11 points), test - 40.
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Faculty of information Technology
Departments
The following departments are involved in teaching the above discipline
Faculty of information Technology