Syllabus for Computer Programming II

Programmeringsteknik II

  • 5 credits
  • Course code: 1TD722
  • Education cycle: First cycle
  • Main field(s) of study and in-depth level: Computer Science G1F, Technology G1F

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2007-03-15
  • Established by:
  • Revised: 2022-02-17
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2022
  • Entry requirements:

    Computer Programming I

  • Responsible department: Department of Information Technology

Learning outcomes

On completion of the course, the student should be able to:

  • use the programming language Python, and tools for testing, debugging and version control, both writing codeand explaining what a given code does;
  • explain the concepts polymorphism and duck typing in Python, and use these concepts in software development;
  • implement recursive solutions to problems;
  • describe the principle for analysing algorithm efficiency and perform analysis on basic algorithms;
  • describe, implement and use the fundamental data structures lists, hash tables and trees and implement abstract data types such as stacks, queues, generators and dictionaries;
  • describe the underlying principles for exception-based error handling and to be able to use it in programs;
  • explain the limitations in Python and ways to integrate Python with other languages;
  • implement programs with both sequential and parallel or event-driven flows, including simple parallel processing, and discuss the differences.


Continued programming in Python: polymorphism, duck typing, exceptions. The concepts of stacks, queues, simple graphs such as lists and trees, hash tables, dictionaries in general with Python's Dictionary as a special case. Generators supported by the yield keyword.

Basic algorithms for storage, searching and sorting. Debugging, versioning and testing. Asymptotic time complexity analysis. Characteristics of Python as a language and programming environment compared to C++. Implementation and basic performance comparisons for identical algorithms in Python and C++.

Code represented as objects, including lambda expressions, and the use of such constructs for event-driven and parallel programming in Python.


Lectures, problem classes/computer lab and compulsory assignments.


Written examination (2 credits) and approved assignments (3 credits).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicatedand allow a student to be assessed by another method. An example of special reasons might be a certificate regardingspecial pedagogical support from the disability coordinator of the university.

Reading list

The reading list is missing. For further information, please contact the responsible department.