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Applied Data Science (SECU0057)

Key information

Faculty
Faculty of Engineering Sciences
Teaching department
Security and Crime Science
Credit value
15
Restrictions
Students are not required to have any prior knowledge of programming, but they should be willing to learn to program and understand that modules require a considerable amount of programming. External students should email the department with a brief statement on why they want to take the module and places will be dependent on eligibility and capacity.
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

The module will introduce students to the domain of data science relevant to the field of crime and security science. It will give them a broad overview of (i) web data collection techniques using APIs and web-scraping, (ii) text mining (iii) machine learning principles and methods, (iv) as well as guest lectures on applications of natural language processing to crime and security science. The techniques covered in the module are taught within the context of their application to crime and security, but the course is suitable for students from other disciplines. The module consists of ten combined sessions (lectures + practical tutorial sessions).听 During the weekly practical tutorial sessions, students will implement the techniques they have been taught in lecturers using the R programming language. The module is suitable for students intending to learn about data science and acquire the skills to conduct a full data science project from start to finish. This is a practical module requiring a considerable amount of programming to complete the weekly tutorial exercises, homework, and a large assessment in R. Some preparation material and programming tasks will be provided to help students master the basics of the R language. Students must work through the module preparation material and complete all the required tasks before the module begins.

Module deliveries for 2024/25 academic year

Intended teaching term: Term 2 听听听 Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study
In person
Methods of assessment
70% Coursework
30% In-class activity
Mark scheme
Numeric Marks

Other information

Number of students on module in previous year
23
Module leader
Dr Nilufer Tuptuk
Who to contact for more information
scs-teaching@ucl.ac.uk

Last updated

This module description was last updated on 8th April 2024.

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Important information

The catalogue has been updated with key information about the modules that will run during the 2024/25 academic session. Current and prospective students can browse through the catalogue to consider possible module choices for the coming year.

Please note,听information in the catalogue is听subject to change as teaching and assessment arrangements for 2024/25 may need to be adjusted in line with the University's Feedback and Assessment principles and operating model.

Centrally managed exam durations will be provided on students' individual exam timetables. Arrangements for locally managed exams and in-class activity will be confirmed by the teaching department for the module.

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