2024/2025
Intro to Programming in Python
Type:
Mago-Lego
Delivered by:
School of Data Analysis and Artificial Intelligence
When:
4 module
Open to:
students of one campus
Instructors:
Majid Sohrabi
Language:
English
ECTS credits:
3
Contact hours:
32
Course Syllabus
Abstract
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.
Learning Objectives
- - 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.
Expected Learning Outcomes
- 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
Assessment Elements
- 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.
Bibliography
Recommended Core Bibliography
- 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
- 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
- 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
- Zinoviev, D., & Tulton, A. O. (2018). Complex Network Analysis in Python : Recognize - Construct - Visualize - Analyze - Interpret. Pragmatic Bookshelf.
Recommended Additional Bibliography
- 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