Suspended in 2025/26
ST101 Half Unit
Programming for Data Science
This information is for the 2025/26 session.
Course Convenor
Christine Yuen
Availability
This course is available on the BSc in Accounting and Finance, Erasmus Reciprocal Programme of Study and Exchange Programme for Students from University of California, Berkeley. This course is available with permission as an outside option to students on other programmes where regulations permit. This course is available with permission 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.
Requisites
Additional requisites:
Although not a formal requirement, it is preferable that students have some familiarity with the basic concepts of probability and statistics, to the level of ST102/ST107 first 2 chapters (Data visualisation and descriptive statistics and probability theory).
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, testing and debugging, errors and exception handling, and data input-output operations using file systems and operating system standard input-output; 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 may also discuss and provide references to how the fundamental programming concepts are implemented in other programming languages, in particular, R.
Teaching
20 hours of seminars and 20 hours of lectures in the Autumn Term.
Students are required to install Python on their own laptops and use their own laptops in the classes and lectures.
Formative assessment
Students will be expected to produce 5 problem sets in the AT.
The problem sets will consist of computer programming exercises in Python programming language.
Indicative 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
- P. Gries, J. Campbell, and J. Montojo, Practical programming: an introduction to computer science using Python 3.6, Pragmatic Bookshelf, 2017
Additional reading
- W. Mckinney, Python for Data Analysis, 2nd Edition, O'Reilly, 2017
- 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
Assessment
Exam (70%), duration: 120 Minutes, reading time: 15 minutes in the January exam period
Continuous assessment (30%)
The exam will be an invigilated on-campus 'e-exam'.
Key facts
Department: Statistics
Course Study Period: Autumn Term
Unit value: Half unit
FHEQ Level: Level 4
CEFR Level: Null
Total students 2024/25: 149
Average class size 2024/25: 25
Capped 2024/25: Yes(150)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.
Personal development skills
- Self-management
- Problem solving
- Application of information skills
- Communication
- Application of numeracy skills
- Commercial awareness
- Specialist skills