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 Academic 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 2022/23, 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 ( Seminar/Tutorial Hours 10, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 88 )
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 Not entered
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 On completion of the course students should be able to:

A. Knowledge and Understanding

-Apply statistical analyses to data and draw conclusions about large populations based only on information obtained from samples

-Understand and test the assumptions behind various hypothesis testing techniques and apply them appropriately to draw inference from data

-Apply knowledge of different discrete and continuous probability distributions, together with descriptive statistics, to summarise, explore and interpret data

B. Practice: applied knowledge, skills and understanding:

-Define research questions based on real data.

-Critically assess the data analytics approaches to apply to the data and draw appropriate conclusions and managerial recommendations from the analytics results.

-Document their findings in a concise and scientific manner.

C. Communication and ICT skills

-Apply state-of-the-art tools for statistical analyses.

-Understand and recognise the theoretical foundations behind the tools available in statistical software

-Present to an audience a full data analytics project starting with the definition of research questions and going through the application of analytics techniques and proposing managerial recommendations.
KeywordsNot entered
Course organiserDr Xin Fei
Tel: (0131 6)50 8074
Course secretaryMs Heather Ferguson
Tel: (0131 6)50 8074
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