Course content

The course is an introduction to procedure-oriented programming in Python.

Topics:

• Variables and data types.
• Input and output.
• Control structures: Sequence, conditional program flow and repetitions.
• Algorithms. Structuring and modularization of programs; functions and modules.
• Use of libraries and frameworks.
• Data structures: Lists, tables, text strings, sets, tuples and dictionaries.
• Persistent storage of data, file input and output, and exceptions.
• Recursion, sorting and searching.
• Formulation of algorithms as pseudo code or in flow diagrams.
• Basic use of Numpy and Matplotlib.
• Python as a programming environment.
• Basic strategies for testing and debugging.
• Version control using git.

Learning outcome

Knowledge:

• Hold basic knowledge about the basic elements of procedure-oriented programming.
• Hold basic knowledge about the process from a problem to a working program.
• Familiarity with object oriented programming.
• Familiarity with version control systems.
• Can explain some common ways that AI can be used in programming.

Skills:

• Be able to use the basic elements in practical, procedure-oriented programming.
• Be able to use object-oriented libraries and their method-calls.
• Be able to use relevant programming tools, like Thonny or other syntax-driven editors with semantic error-tagging and step-wise execution with inspection of variables.
• For small-scale problems, be able to use the process from analysis, via algorithm design formulated as pseudo code or in flow-charts, before programming in Python, and testing whether the solution works.
• Be able to carry out small programming projects with a few hundred lines of code.
• Be able to explain your own code to others and AI, and give constructive feedback to others' code.
• An ability to reflect on the approprateness of the use of AI as a tool to write code and for learning.

Learning methods and activities

Group activities, exercise lectures and mandatory exercises.

Compulsory assignments

• Exercises

Recommended previous knowledge

None.

Course materials

Announced at the start of semester.

Credit reductions