MY570      Half Unit
Computer Programming

This information is for the 2022/23 session.

Teacher responsible

Dr Milena Tsvetkova COL8.06


This course is available on the MPhil/PhD in International Relations and MPhil/PhD in Social Research Methods. This course is available with permission as an outside option to students on other programmes where regulations permit.

This course is not controlled access. If you register for a place and meet the prerequisites, if any, you are likely to be given a place.

Course content

This course introduces students to the fundamentals of computer programming as students design, write, and debug computer programs using the programming language Python. The course will also cover the foundations of computer languages, algorithms, functions, variables, objectorientation, scoping, and assignment. The course will rely on practical examples from computational social science and social data science.

Students will learn how to design algorithms to solve problems and how to translate these algorithms into working computer programs. Students acquire skills and experience as they learn Python, through programming assignments with an approach that integrates project-based learning. This course is an introduction to the fundamental concepts of programming for students who lack a formal background in the field, but will include more advanced problem-solving skills in the later stages of the course. Topics include algorithm design and program development; data types; control structures; functions and parameter passing; recursion; computational complexity; searching and sorting; and an introduction to the principles of object-oriented programming. The primary programming languages used in the course will be Python.


This course is delivered through a combination of classes and lectures totalling a minimum of 25 hours across Michaelmas Term.

This course has a reading week in Week 6 of MT.

Formative coursework

Students will be expected to produce 9 problem sets in the MT.

Students will work on weekly, structured problem sets in the staff-led class sessions. Example solutions will be provided at the end of each week.

Indicative reading

  • Guttag, John V. Introduction to Computation and Programming Using Python: With Application to Understanding Data. MIT Press, 2016.
  • Gries, Paul, Jennifer Campbell, and Jason M Montojo. Practical Programming: An Introduction to Computer Science Using Python 3. The Pragmatic Bookshelf, 2013.
  • Miller, Bradley N. and David L. Ranum. Problem Solving with Algorithms and Data Structures Using Python. Available online at
  • Python, Intermediate and advanced documentation at


Project (50%) and problem sets (50%) in the MT.

For the individual project, students will be required to develop Python software that addresses a sufficiently complex computational social science task. Examples of possible projects include a software package that collects and analyses online data, an experimental game, or an agent-based model. Marking of this assessment will be at a level appropriate for PhD students.

Key facts

Department: Methodology

Total students 2021/22: 2

Average class size 2021/22: 1

Value: Half Unit

Guidelines for interpreting course guide information

Course selection videos

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Personal development skills

  • Self-management
  • Team working
  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills
  • Specialist skills