MY470      Half Unit
Computer Programming

This information is for the 2021/22 session.

Teacher responsible

Dr Milena Tsvetkova COL8.06


This course is compulsory on the MSc in Applied Social Data Science. This course is available on the MSc in Data Science, MSc in Geographic Data Science, MSc in Human Geography and Urban Studies (Research) and MSc 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, object­-orientation, 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 year, some or all of this teaching may be delivered through a combination of virtual classes and flipped-lectures delivered as short online videos.

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


Take-home assessment (50%) and problem sets (50%) in the MT.

Course selection videos

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.

Important information in response to COVID-19

Please note that during 2021/22 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the differing needs of students in attendance on campus and those who might be studying online. For example, this may involve changes to the mode of teaching delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.

Key facts

Department: Methodology

Total students 2020/21: 101

Average class size 2020/21: 26

Controlled access 2020/21: No

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

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