Postgraduate Course: Principles of Data Analytics (CMSE11432)
||College||College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Not available to visiting students
|Summary||This course provides students with fundamental theories in probability and inferential statistics and guides students on how to apply them to business analytics problems.
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
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| For MSc Business Analytics students, or by permission of course organiser. Please contact the course secretary.
Course Delivery Information
|Academic year 2020/21, Not available to visiting students (SS1)
||Block 1 (Sem 1)
|Learning and Teaching activities (Further Info)
Lecture Hours 10,
Seminar/Tutorial Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Individual Coursework (100% weighting) Assesses Learning Outcomes 1 to 5.
This will be a group work with individual submissions. Students will be required to propose a topic as a group, with every member working on a different research question within that topic.
||The assessments will be marked according to the University common marking scheme. Feedback on formative assessed work will be provided in line with the Taught Assessment Regulation turnaround period, or in time to be of use in subsequent assessments within the course, whichever is sooner. Summative marks will be returned on a published timetable, which will be communicated to students during semester.
|No Exam Information
On completion of this course, the student will be able to:
- Discuss the concept and methods of data analytics using the proper terminology
- Perform data exploration through statistical and probabilistic methods and formulate data-motivated research questions
- 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
- Formulate managerial guidelines from the answers to research questions and make recommendations
- Communicate findings effectively and efficiently to a critical audience
|Basic Business Statistics: Concepts and Applications (by David M. Levine, Timothy C. Krehbiel, Mark L. Berenson)|
|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.
|Course organiser||Dr Aakil Caunhye
|Course secretary||Ms Emily Davis
Tel: (0131 6)51 7112