Data Analytics in Business and Industry (ODL) STATS5079

  • Academic Session: 2019-20
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Available to Erasmus Students: No
  • Taught Wholly by Distance Learning: Yes

Short Description

The course introduces students to applications of data analytics in business and industry and introduces students to the social, ethical, legal, and professional issues arising in data science. It also delivers experience in the communication and presentation of results.

Timetable

The course mostly consists of asynchronous teaching material.

Requirements of Entry

The course is only available to online-distance learning students on the PGCert/PGDip/MSc in Data Analytics.

Excluded Courses

-/-

Co-requisites

-/-

Assessment

100% Continuous Assessment

This will typically be made up of two reports (40%), two homework exercises including online quizzes (40%) and one presentation (20%). Full details are provided in the programme handbook.

Course Aims

The aims of this course are:

■ to showcase how data-analytical methods are used in industry using different case studies from domains such as marketing or finance;

■ to introduce students to the social, ethical, legal, and professional issues arising in data science;

■ to help students to critically integrate the knowledge acquired in the other courses taught in this programme;

■ to prepare students for their possible future role as practising data analysts;

■ to provide the opportunity for active and independent learning.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ explain and apply a selection of data analytical methods specific to a given application area such as credit scoring or marketing;

■ critically collate results and conclusions;

■ communicate conclusions from data analyses effectively, taking into account different types of target audience;

■ develop, present and critically reflect upon arguments on social, ethical, legal and professional issues in data science.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.