Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

Data Science Fundamentals COMPSCI2028

  • Academic Session: 2022-23
  • School: School of Computing Science
  • Credits: 10
  • Level: Level 2 (SCQF level 8)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

This course is intended for Graduate Apprenticeship students only.

 

This course introduces students to data science, with topics covering a range of mathematical concepts involved in reviewing and changing data. There will be discussions about different types and styles of data and how to handle them, particularly with respect to plotting and visualising this data.

Timetable

None

Requirements of Entry

Entry to Level 2 is guaranteed to students who achieve a GPA of D3 or better in their level 1 courses at the first sitting.

Excluded Courses

None

Assessment

Written examination 70%, mid semester class test 10%, in-class quizzes 5% and course work 15%

Main Assessment In: April/May

Course Aims

This course introduces students to data science, with topics covering a range of mathematical concepts involved in reviewing and manipulating data. There will be discussions about different types and styles of data and how to handle them, particularly with respect to plotting and visualising this data.

Intended Learning Outcomes of Course

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

1. Apply knowledge of tensor form, vectorisation and matrix decomposition in solving mathematical and practical problems.

2. Describe how to bridge the continuous and discrete worlds, solving graph flow via metric operations in a software program.

3. Create effective, clear, and precise visualisations of data and be able to apply manipulations and conversions on said visualisations.

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.