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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Introduction to Research in Data Science (INFR11105)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits30 ECTS Credits15
SummaryThis course provides students with an overview of current research topics in data science. This overview is provided by guest lectures from researchers working throughout different areas of data science, including databases, machine learning, maths, natural language processing, computer vision, speech processing, and related areas.

Second, this course also features a small project to provide students with experience in applying data science methods. The goal of the project is to apply an existing data science method to a interesting real or realistic problem. The student will produce a short project report and poster presentation based on the project.
Course description Not entered
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements For students on the MSc by Research in Data Science only.
Course Delivery Information
Academic year 2015/16, Not available to visiting students (SS1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 300 ( Lecture Hours 18, Seminar/Tutorial Hours 12, Programme Level Learning and Teaching Hours 6, Directed Learning and Independent Learning Hours 264 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Students will receive two types of assessment: formative and summative. The formative assessment will consist of group discussion and feedback on both the materials in the guest lectures and on students' progress in the small practical project.

The summative assessment will evaluate two pieces of work:

1. The 12-15 page project report which will be marked based on how well it covers:
-Purpose: a statement of the problem to be addressed, including arguments as to why solving the problem is important, with reference to ultimate industrial or societal impact.
-Background: a short description of how previous work addresses (or fails to address) this problem, leading to a rationale for the project.
-Methods: A description of the methods and techniques that were used.
-Evaluation: Details of the metrics by which the outcomes of the project were evaluated.

2. The poster presentation will be at a public event, open to University staff and students. The students' poster presentations will be numerically assessed, with brief written feedback.

Breakdown of Assessment:
Written report 80%, Poster 20%
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Be able to identify current research issues and trends in data science.
  2. Gain increased fluency with main ideas and concepts across the different disciplines that make up data science.
  3. Gain experience in applying data science methods in practice. Develop skills in report writing and presentation writing.
Reading List
Additional Information
Course URL
Graduate Attributes and Skills Not entered
KeywordsNot entered
Course organiserDr Charles Sutton
Tel: (0131 6)51 5634
Course secretaryMiss Maree Matheson
Tel: (0131 6)50 9989
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