Delivered at:: School of Data Analysis and Artificial Intelligence

Course type:: Compulsory course

When:: 1 year, 4 module

Instructor

Sohrabi, Majid

Full Syllabus Ask Question

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

Course Contents

Methods
Data types

Assessment Elements

Quiz
In 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 Test
The final test is 10-point scale exam which takes place at the end of the course.

Interim Assessment

2024/2025 4th module
0.6 * Final Test + 0.4 * Quiz

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

Authors

SOHRABI MAJID
Антропова Лариса Ивановна
KOGAN ALEKSANDRA SERGEEVNA
Larionov Aleksandr Vitalevich

Master’s Programme 'Population and Development'

Intro to Programming in Python