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DRPS : Course Catalogue : Business School : Common Courses (Management School)

Postgraduate Course: Principles of Data Analytics (CMSE11358)

Course Outline
SchoolBusiness School CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits15 ECTS Credits7.5
SummaryThis is a compulsory course for the MSc in Business Analytics programme. The course will provide students with the foundations of data analytics and shall cover concepts, visualisation, modelling and analysis of data, and applications in business and economics.
Course description This course aims at training students in the field of data analytics to respond to the job market needs using a variety of analytics techniques. In this era of big data, students will learn how to crunch an incomprehensible amount of information to gain valuable insight. The course covers the typical methodological steps of data analysis along with a variety of data analytics techniques for extracting hidden information and building intelligence to assist with decision making. The course also provides students with the methods and the tools to address common practical issues faced by data analysts.
The objective of this course is to enhance students' understanding of the importance of adopting a series of sound methodological steps in analysing data and to provide them with an artillery of data analytics techniques along with hands-on experience in using them. The course provides opportunities for students to learn from each other, from practitioners in the field, and from the latest theoretical and applied research in the field. The course will require students to work in groups on realistic projects in different business settings, and to present their work to the rest of the class and to an external panel when the projects are supplied by industry.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2018/19, Not available to visiting students (SS1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 150 ( Lecture Hours 20, Seminar/Tutorial Hours 10, Programme Level Learning and Teaching Hours 3, Directed Learning and Independent Learning Hours 117 )
Assessment (Further Info) Written Exam 30 %, Coursework 70 %, Practical Exam 0 %
Additional Information (Assessment) Examination
Final exam 30% weighting
Term projects 60% weighting
Presentations 10% weighting

-Term projects (60% of the mark including a peer assessment component worth 10%) in which students will have to undertake a term project involving problem statement, formulation of research questions, data collection and analysis, report on findings, formulation of recommendations and managerial guidelines.
-Presentations (10% of the final mark) involving the effective and efficient communication of findings to a critical audience to demonstrate their ability to address real world problems and to convince their line managers or sponsors to implement the proposed recommendations
-Exam(s) (30% of the final mark)
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)Principles of Data Analytics2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Discuss the concept and methods of data analytics using the proper terminology
  2. Identify and properly state relevant research questions related to business problems in different settings
  3. Analyse the data relevant to research problems and related research questions, critically discuss alternative data analytics approaches and methods and choose the right techniques to address research questions and to build intelligence for decision making
  4. Formulate managerial guidelines from the answers to research questions and make recommendations
  5. Communicate findings effectively and efficiently to a critical audience
Reading List
Additional Information
Graduate Attributes and Skills Not entered
Course organiserDr Aakil Caunhye
Course secretaryMiss Lauren Millson
Tel: (0131 6)51 3013
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