Dr Christine Yuen

This course is compulsory on the BSc in Data Science and BSc in Politics and 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) and demand is typically very high. Students who have this course as a compulsory course are guaranteed a place. 

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).

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. 

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 course includes a reading week in Week 6 of Michaelmas Term.

Students are required to install Python on their own laptops and use their own laptops in the classes and lectures.

Students will be expected to produce 8 problem sets in the MT.

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

• 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

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

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

Students are required to hand in solutions to 2 problem sets using Python, each accounting for 15% of the final assessment.

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