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Programming in R: Applications for Speech Data Processing (PALS0047)

Key information

Faculty
Faculty of Brain Sciences
Teaching department
Division of Psychology and Language Sciences
Credit value
15
Restrictions
Priority will be given to students from within PALS MSc Language Sciences and MA Linguistics. Students from other divisions are accepted only if space is available.
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

Modern computers are incredibly powerful and fast, capable of executing many billions of operations each second. Although fast, they are not intelligent: computers need to be told exactly what to do in order to carry out their job correctly. Programming is the process of creating a set of instructions that tell a computer exactly how to perform a given task. The ability to program is becoming increasingly important for speech and language researchers, and experience with programming substantially increases one’s marketability to potential employers both in academia and in industry. This module provides an introduction to programming methods and practices in R, a powerful computing language that is used by scientists throughout the world. Rather than learning programming principles through working with generic data sets and seeking research goals that may not be applicable to speech science (as might be encountered in introductory programming modules in external faculties), PALS students will learn programming fundamentals through the lens of speech science research. By working with a wide range of speech data and tackling real problems faced in analysing such data, the skills gained in this module will be immediately applicable to research in the field of speech science. Topics will include an introduction to the R environment; variables, functions, and classes; statements; parametrization; and data wrangling. The course is designed for psychology and linguistics students with little to no prior programming expertise.

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Module deliveries for 2024/25 academic year

Intended teaching term: Term 1 ÌýÌýÌý Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study
In person
Methods of assessment
80% Fixed-time remote activity
20% Other form of assessment
Mark scheme
Numeric Marks

Other information

Number of students on module in previous year
22
Module leader
Dr Chris Carignan
Who to contact for more information
pals.modules@ucl.ac.uk

Last updated

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

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