MT4561 The History and Future of Data

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.

Planned timetable

9.00 am Mon (even weeks), Tue and Thu

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

Module coordinator

Dr D A Kent

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

Module description

The collection and analysis of data pervade contemporary life. This course will explore the emergence of now-standard statistical techniques and the development of approaches to data from the seventeenth to twenty-first centuries.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST PASS MT1007 OR PASS MT2508 OR PASS EC2203 OR PASS PH3012

Co-requisites

IF NOT ALREADY PASSED YOU MUST TAKE 1 MODULE FROM {MT3501, MT3502, MT3503, MT3504, MT3505, MT3506, MT3507, MT3508}

Assessment pattern

Coursework - 50%, Written exam - 50%

Re-assessment

Coursework - 100%

Learning and teaching methods and delivery

Weekly contact

2.5 Hours (x10 weeks); Tutorials: 1 Hour (x10 weeks).

Scheduled learning hours

35

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

114

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • relate data practices to their wider historical and cultural contexts and understand the circumstances surrounding their creation and reception
  • critically assess and evaluate different types of source material
  • assess how historical data science techniques have shaped contemporary applications
  • evaluate how particular collections, representations, or publications of data represent or exclude individual values