MDA204 Introduction to Python

Faculty of Science
Spring 2025
Extent and Intensity
0/0/0. 6 credit(s). Type of Completion: z (credit).
Teacher(s)
Mgr. Tomáš Foltýnek, Ph.D. (lecturer)
Guaranteed by
Mgr. Tomáš Foltýnek, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics
Prerequisites
There are no specific requirements to enrol on the course, but the content presumes previous programming experience at least on a basic level.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The aim of the course is to provide students with an introduction to the Python programming language with a focus on data analytics. The students will learn how to write Python programs to solve real-world data analytics problems. They will also understand the fundamentals of object-oriented programming and how it can be applied to data analytics. Finally, the students will develop skills in data cleaning, data wrangling, data analysis, and data visualization using Python.
Learning outcomes
After successful completion of the course, students will:
- have gained proficiency in Python programming;
- understand OOP concepts;
- have gained practical skills in using standard Python libraries for data cleaning, data wrangling, data analysis, and data visualization
Syllabus
  • 1. Introduction to Python (Installing Python, Basic syntax and data types, Variables and expressions, Control structures (if-else, loops), Functions and modules)
  • 2. Object-Oriented Programming (Classes and objects, Inheritance and polymorphism, Encapsulation and data hiding, Abstraction and interfaces)
  • 3. Data Cleaning and Preparation with Python(Reading and writing data in various formats (CSV, JSON, etc.), Data types and data structures, Handling missing values, Data transformation and manipulation)
  • 4. Data Analysis with Python (Data analysis libraries (NumPy, Pandas, Matplotlib), Data exploration and visualization, Basic statistical analysis)
  • 5. Data Wrangling with Python (Advanced data transformation and manipulation, Merging and joining datasets, Grouping and aggregating data)
Literature
    required literature
  • MCKINNEY, Wes. Python for data analysis : data wrangling with pandas, NumPy and Jupyter. Third edition. Sebastopol, CA: O'Reilly Media, 2022, xvi, 561. ISBN 9781098104030. info
  • VANDERPLAS, Jacob T. Python data science handbook : essential tools for working with data. First edition. Tokyo: O'Reilly, 2017, xvi, 529. ISBN 9781491912058. info
    recommended literature
  • SWEIGART, Albert. Automate the boring stuff with Python : practical programming for total beginners. 2nd edition. San Francisco: No Starch Press, 2020, xxxix, 547. ISBN 9781593279929. info
  • GUZDIAL, Mark and Barbara ERICSON. Introduction to computing & programming in Python : a multimedia approach. 2nd ed. Upper Saddle River [N.J.]: Prentice Hall, 2010, xxiii, 401. ISBN 9780136060239. info
  • ZELLE, John M. Python programming : an introduction to computer science. Wilsonville: Franklin, Beedle &Associates, 2004, xiv, 514. ISBN 1887902996. info
Teaching methods
Pre-recorded lectures and task assignments, synchronous consultations, asynchronous discussions, individual homework, individual project, and project presentation.
Assessment methods
The final mark will be composed of:
30% regular homework,
30% individual practical project + presentation,
40% final supervised written exam (theoretical + practical)
For the successful completion of the course, at least 60% points of each part are required.
Language of instruction
English
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