PN4106 Data Science for Psychology & Neuroscience

Academic year

2024 to 2025 Semester 1

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 10

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

"Available only to students in the second year of the Honours Programme, Research Methods in Psychology or Master of Research Neuroscience programmes."

Planned timetable

Wednesdays 2-4pm

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr J M Ales

Dr J M Ales
This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Dr Justin Ales

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

Data science has become a critical part of scientific and industry working. This module will introduce students to modern data science methods such as machine learning and data mining. Emphasis will be given to the practical utilisation of these methods in the context of psychology and neuroscience.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST ( TAKE PS3021 OR TAKE PS3023 OR TAKE PN3025 ) AND ( TAKE PS3022 OR TAKE PS3024 OR TAKE PN4026 ) OR TAKE PN3322

Assessment pattern

Coursework = 100%

Re-assessment

"Coursework=100%, Re-assessment applies to failed components only"

Learning and teaching methods and delivery

Weekly contact

"1 lecture, 1 practical (x10 weeks)"

Intended learning outcomes

  • "By the end of the module, students will be able to understand basic methods for data science analysis"
  • "By the end of the module, students will be able to implement basic data science analysis methods"
  • "By the end of the module, students will be able to recognise common difficulties that arise with data"
  • "By the end of the module, students will be able to understand ethical issues that occur when using data science methods in society"