Timetable information in the Course Catalogue may be subject to change.

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : Business School : Common Courses (Management School)

Postgraduate Course: Principles of Data Analytics (CMSE11432)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis course provides students with fundamental theories in probability and inferential statistics and guides students on how to apply them to business analytics problems.
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 from data samples 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 focus is on understanding the underlying principles behind statistical analyses of data. The course provides opportunities for students to learn from each other and from the latest theoretical and applied research in the field.

Outline Content

This course consists of 5 lectures.
(Lecture 1) Fundamentals in Statistics and Probability
(Lecture 2) Probability (continue)
(Lecture 3) Hypothesis testing
(Lecture 4) Analysis of variance
(Lecture 5) Linear regression

Student Learning Experience

Tutorial/seminar hours represent the minimum total live hours - online or in-person - a student can expect to receive on this course. These hours may be delivered in tutorial/seminar, lecture, workshop or other interactive whole class or small group format. These live hours may be supplemented by pre-recorded lecture material for students to engage with asynchronously.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements For MSc Business Analytics and MSc Finance, Technology and Policy students only, or by permission of course organiser. Please contact the course secretary.
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2024/25, Not available to visiting students (SS1) Quota:  None
Course Start Block 1 (Sem 1)
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 5, Seminar/Tutorial Hours 10, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 83 )
Additional Information (Learning and Teaching) Seminar/Tutorial hrs are the min total live hrs, online or in-person, students can expect to receive
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 70% coursework (group) - assesses all course Learning Outcomes
30% coursework (individual) - assesses course Learning Outcomes 3, 4, 5
Feedback Formative: Feedback will be provided throughout the course.

Summative:Feedback will be provided on the assessments within agreed deadlines.
No Exam Information
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. Perform data exploration through statistical and probabilistic methods and formulate data-motivated research questions
  3. Analyse the data relevant to problems, 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
Basic Business Statistics: Concepts and Applications (by David M. Levine, Timothy C. Krehbiel, Mark L. Berenson)

Resource List:
Additional Information
Graduate Attributes and Skills Communication, ICT, and Numeracy Skills

After completing this course, students should be able to:

Critically evaluate and present digital and other sources, research methods, data and information; discern their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of organisational contexts.

Knowledge and Understanding

After completing this course, students should be able to:

Demonstrate a thorough knowledge and understanding of contemporary organisational disciplines; comprehend the role of business within the contemporary world; and critically evaluate and synthesise primary and secondary research and sources of evidence in order to make, and present, well informed and transparent organisation-related decisions, which have a positive global impact.

Identify, define and analyse theoretical and applied business and management problems, and develop approaches, informed by an understanding of appropriate quantitative and/or qualitative techniques, to explore and solve them responsibly.
KeywordsNot entered
Course organiserDr Antonia Gieschen
Course secretaryMr Ewan Henderson
Help & Information
Search DPTs and Courses
Degree Programmes
Browse DPTs
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Important Information