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Application Programming (курсова робота)
Major: Computer Sciences
Code of Subject: 6.122.00.O.12
Credits: 2
Department: Artificial Intelligence Systems
Lecturer: Vyklyuk Ya.
Semester: 1 семестр
Mode of Study: денна
Learning outcomes:
know:
- Basic features and limitations of the programming language
- Data types and basic language constructs
- Implementation of the concept of object-oriented programming in Python
- how to interact with different databases and the file system
be able:
- Create your own simple product using the concept of object-oriented
programming and asynchronous processing
- create a module that can be used by third parties
- work with popular relational and non-relational databases using models written by Python
- use multi-threaded and multi-core processors to develop software with help Python
-Cover code with tests
- Basic features and limitations of the programming language
- Data types and basic language constructs
- Implementation of the concept of object-oriented programming in Python
- how to interact with different databases and the file system
be able:
- Create your own simple product using the concept of object-oriented
programming and asynchronous processing
- create a module that can be used by third parties
- work with popular relational and non-relational databases using models written by Python
- use multi-threaded and multi-core processors to develop software with help Python
-Cover code with tests
Required prior and related subjects:
Algoritmization and programming
Summary of the subject:
TYPES OF DATA AND COLLECTIONS. FUNCTIONS AND MODULES. WORKING WITH FILES. OBJECT-ORIENTED PROGRAMMING. MULTI-FLOW AND ASYNCHRONITY. CODE TESTING. PYTHON FOR SCIENTISTS. ANACONDA DISTRIBUTION AND CONDA PACKAGE MANAGER. PANDAS LIBRARY. NumPy LIBRARY. LIBRARY Matplotlib
Recommended Books:
1. Allen Downey. Think Python, 2nd Edition. How to Think Like a Computer Scientist / O’Reilly, 2015. - 289 p.
2. Charles R. Severance. Python for Everybody: Exploring Data in Python 3 / CreateSpace
Independent Publishing Platform, 2016. - 244 pages
3. Wes McKinney & PyData Development Team. pandas: powerful Python data analysis toolkit
2. Charles R. Severance. Python for Everybody: Exploring Data in Python 3 / CreateSpace
Independent Publishing Platform, 2016. - 244 pages
3. Wes McKinney & PyData Development Team. pandas: powerful Python data analysis toolkit
Assessment methods and criteria:
50 - labs,
50 - exam
50 - exam
Application Programming (курсова робота)
Major: Computer Sciences
Code of Subject: 6.122.00.O.11
Credits: 2
Department: Computer-Aided Design
Lecturer:
Semester: 1 семестр
Mode of Study: денна
Learning outcomes:
Required prior and related subjects:
Summary of the subject:
Recommended Books:
Assessment methods and criteria:
Application Programming (курсова робота)
Major: Computer Sciences
Code of Subject: 6.122.00.O.10
Credits: 2
Department: Automated Control Systems
Lecturer:
Semester: 1 семестр
Mode of Study: денна
Learning outcomes:
Required prior and related subjects:
Summary of the subject:
Recommended Books:
Assessment methods and criteria:
Application Programming
Major: Computer Sciences
Code of Subject: 6.122.00.O.7
Credits: 5
Department: Automated Control Systems
Lecturer:
Semester: 1 семестр
Mode of Study: денна
Learning outcomes:
Required prior and related subjects:
Summary of the subject:
Recommended Books:
Assessment methods and criteria:
Application Programming
Major: Computer Sciences
Code of Subject: 6.122.00.O.8
Credits: 5
Department: Computer-Aided Design
Lecturer:
Semester: 1 семестр
Mode of Study: денна
Learning outcomes:
Required prior and related subjects:
Summary of the subject:
Recommended Books:
Assessment methods and criteria:
Application Programming
Major: Computer Sciences
Code of Subject: 6.122.00.O.9
Credits: 5
Department: Artificial Intelligence Systems
Lecturer: Vyklyuk Ya.
Semester: 1 семестр
Mode of Study: денна
Learning outcomes:
know:
- Basic features and limitations of the programming language
- Data types and basic language constructs
- Implementation of the concept of object-oriented programming in Python
- how to interact with different databases and the file system
be able:
- Create your own simple product using the concept of object-oriented
programming and asynchronous processing
- create a module that can be used by third parties
- work with popular relational and non-relational databases using models written by Python
- use multi-threaded and multi-core processors to develop software with help Python
-Cover code with tests
- Basic features and limitations of the programming language
- Data types and basic language constructs
- Implementation of the concept of object-oriented programming in Python
- how to interact with different databases and the file system
be able:
- Create your own simple product using the concept of object-oriented
programming and asynchronous processing
- create a module that can be used by third parties
- work with popular relational and non-relational databases using models written by Python
- use multi-threaded and multi-core processors to develop software with help Python
-Cover code with tests
Required prior and related subjects:
Algoritmization and programming
Summary of the subject:
TYPES OF DATA AND COLLECTIONS. FUNCTIONS AND MODULES. WORKING WITH FILES. OBJECT-ORIENTED PROGRAMMING. MULTI-FLOW AND ASYNCHRONITY. CODE TESTING. PYTHON FOR SCIENTISTS. ANACONDA DISTRIBUTION AND CONDA PACKAGE MANAGER. PANDAS LIBRARY. NumPy LIBRARY. LIBRARY Matplotlib
Recommended Books:
1. Allen Downey. Think Python, 2nd Edition. How to Think Like a Computer Scientist / O’Reilly, 2015. - 289 p.
2. Charles R. Severance. Python for Everybody: Exploring Data in Python 3 / CreateSpace
Independent Publishing Platform, 2016. - 244 pages
3. Wes McKinney & PyData Development Team. pandas: powerful Python data analysis toolkit
2. Charles R. Severance. Python for Everybody: Exploring Data in Python 3 / CreateSpace
Independent Publishing Platform, 2016. - 244 pages
3. Wes McKinney & PyData Development Team. pandas: powerful Python data analysis toolkit
Assessment methods and criteria:
50 - labs,
50 - exam
50 - exam