2026/2027
Введение в программирование на Python
ID 1174007
Статус:
Маго-лего
Где читается:
Факультет компьютерных наук
Охват аудитории:
для своего кампуса
Преподаватели:
Шляхов Фёдор Сергеевич
Язык:
русский
Кредиты:
3
Контактные часы:
32
Программа дисциплины
Аннотация
Python is widely regarded as one of the easiest and most popular programming languages to learn. This course provides a solid understanding of Python's fundamental concepts and constructs, making it an ideal choice for beginners. It serves as a foundation for tackling diverse problems, such as data analysis and software development. With a focus on data science applications, the course covers topics like data importation, storage, manipulation, and essential analysis tools. It is designed for students with limited programming experience, acting as a stepping stone to more specialized areas of Python, including machine learning, statistical data processing, and data visualization.
Цель освоения дисциплины
- - By actively engaging in a significant number of practical exercises both in class and at home, as well as participating fully in group projects, students consistently achieve outstanding results.
Планируемые результаты обучения
- Load and use additional Python modules
- Write their own functions
- Identify and differentiate basic Python data types. Choose the correct data types based on the problem at hand.
- Identify and define basic Python syntax
- Use Jupyter Notebook or a similar program
Содержание учебной дисциплины
- Intro to Anaconda, Jupyter Notebook, and other similar environments
- Data types: integers and strings. Input and output. Strings formatting
- Data types: floating-point numbers and boolean. Logical operators. Conditionals
- Different types of loops
- Data types: lists and tuples. For loop
- Methods I (String)
- Methods II (Lists)
- Data types: sets and dictionaries
- Nested Structures
- Functions
- Working with files in Python
- Methods
- Data types
Элементы контроля
- QuizIn class assignments once a week or once in two weeks, each quiz consists of several questions and a single quiz is 10-points scale
- Final TestThe final test is 10-point scale exam which takes place at the end of the course.
Список литературы
Рекомендуемая основная литература
- Álvaro Scrivano. (2019). Coding with Python. Minneapolis: Lerner Publications ™. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1947372
- Ben Stephenson. (2019). The Python Workbook : A Brief Introduction with Exercises and Solutions (Vol. 2nd ed. 2019). Springer.
- Dr. Ossama Embarak. (2018). Data Analysis and Visualization Using Python : Analyze Data to Create Visualizations for BI Systems. Apress.
- Embarak O. Data Analysis and Visualization Using Python: Analyze Data to Create Visualizations for BI Systems. - Apress, 2018.
- Graph Data Science with Neo4j : learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project, Scifo, E., 2023
- Landau, R. H., Bordeianu, C. C., & Páez Mejía, M. J. (2007). Computational Physics : Problem Solving with Python (Vol. Second revised and enlarged edition). Weinheim: Wiley-VCH. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1016329
- Modeling and simulation in python, Kinser, J. M., 2022
- Mueller, J. (2014). Beginning Programming with Python For Dummies. Hoboken: For Dummies. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=838174
- Pro Deep Learning with TensorFlow 2.0 : a mathematical approach to advanced artificial intelligence in Python, Pattanayak, S., 2023
- Python for data analysis : data wrangling with pandas, numPy, and IPhython, Mckinney, W., 2017
- Python in a nutshell : a desktop quick reference, , 2023
- Raj P. M. K., Mohan A., Srinivasa K.G. (2018) Basics of Graph Theory. In: Practical Social Network Analysis with Python. Computer Communications and Networks. Springer, Cham. Retrieved from https://link.springer.com/chapter/10.1007%2F978-3-319-96746-2_1#citeas
- Richert, W., & Coelho, L. P. (2013). Building Machine Learning Systems with Python. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=619996
- Rubio, D. (2017). Beginning Django : Web Application Development and Deployment with Python. [Berkeley, CA]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1623501
- Severance, C. (2016). Python for Everybody : Exploring Data Using Python 3. Place of publication not identified: Severance, Charles. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsotl&AN=edsotl.OTLid0000336
- Sweigart, Al. Automate the boring stuff with Python: practical programming for total beginners. – No Starch Press, 2015. – 505 pp.
- Taieb, D. (2018). Data Analysis with Python : A Modern Approach. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1993344
- Think Python - How to Think Like a Computer Scientist (Downey) - CCBY4_043 - Allen B. Downey - 2022 - Open Educational Resources: libretexts.org - https://ibooks.ru/products/390563 - 390563 - iBOOKS
- Zinoviev, D., & Tulton, A. O. (2018). Complex Network Analysis in Python : Recognize - Construct - Visualize - Analyze - Interpret. Pragmatic Bookshelf.
Рекомендуемая дополнительная литература
- Derivatives analytics with Python : data analysis, models, simulation, calibration and hedging, Hilpisch, Y. J., 2015
- Explainable AI recipes : implement solutions to model explainability and interpretability with Python, Mishra, P., 2023
- Learning Python : [covers Python 2.5], Lutz, M., 2008
- Pandas for everyone : Python data analysis, Chen, D. Y., 2023
- Python 3, Прохоренок, Н. А., 2016
- Python crash course : a hands-on, project-based introduction to programming, Matthes, E., 2023