ST101      Half Unit
Programming for Data Science

This information is for the 2021/22 session.

Teacher responsible

Dr Christine Yuen


This course is compulsory on the BSc in Data Science. This course is available on the BSc in Actuarial Science, BSc in Finance and BSc in Mathematics, Statistics and Business. This course is available with permission as an outside option to students on other programmes where regulations permit and to General Course students.

This course has a limited number of places (it is capped). Students who have this course as a compulsory course are guaranteed a place. Places for all other students are allocated on a first come first served basis.

Course content

The primary focus of the course is to cover principles of computer programming with a focus on data science applications.

The topic covered will include variables, basic data types, data structures and sequences, control flow structures, modularisation, functions, variable and function scoping, errors and exception handling, and data input-output operations using file systems and operating system standard input-output; use of multi-dimensional arrays via NumPy, data processing using Pandas dataframes; principles of object-oriented programming including objects, classes, methods, encapsulation, inheritance, and polymorphism; principles of functional programming languages such as use of immutable data, flow control using functional calls and recursions; practical aspects of algorithmic concepts such as searching.

The course will primarily use Python programming language, but will also discuss and provide references to how the fundamental programming concepts are implemented in other programming languages, in particular, R. 


This course will be delivered through a combination of classes, lectures and Q&A sessions totalling a minimum of 35 hours across Michaelmas Term. This year, some or all of this teaching may be delivered through a combination of classes and flipped-lectures delivered as short online videos. This course includes a reading week in Week 6 of Michaelmas Term.

Formative coursework

Students will be expected to produce 10 exercises in the MT.

The problem sets will consist of computer programming exercises in Python programming language.

Indicative reading

Essential Reading: 

  • J. V. Guttag, Introduction to Computation and Programming using Python, Second Edition, The MIT Press, 2017
  • A. B. Downey, Think Python: How to Think like a Computer Scientist, 2nd Edition, O'Reilly Media, 2015
  • W. Mckinney, Python for Data Analysis, 2nd Edition, O'Reilly, 2017

Additional Reading: 

  • J. Zelle, Python Programming: An Introduction to Computer Science, 3rd edition, Franklin, Beedle & Associates, 2016
  • M. Lutz, Learning Python, 5th Edition, O'Reilly Media, 2013
  • M. Dawson, Python Programming for the Absolute Beginner, 3rd Edition, Course Technology, 2010


Coursework (30%) in the MT.
Project (70%) in the LT.

Students are required to hand in solutions to 3 sets of exercises using Python, each accounting for 10% of the final assessment.

The project will require from students to solve a practical programming task, which will allow them to apply the concepts learned in the course and demonstrate their knowledge. 

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: Statistics

Total students 2020/21: 53

Average class size 2020/21: 19

Capped 2020/21: Yes (60)

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Self-management
  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills
  • Commercial awareness
  • Specialist skills