Data Science Fundamentals COMPSCI2028

  • Academic Session: 2024-25
  • School: School of Computing Science
  • Credits: 10
  • Level: Level 2 (SCQF level 8)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Collaborative Online International Learning: 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.