Postgraduate Course: Principles of Data Analytics (CMSE11432)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course provide students with fundamental theory of probability and statistics and how it applies to industrial 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, from practitioners in the field, and from the latest theoretical and applied research in the field.
Outline Content: This course consists of 5 lectures.
(Lecture 1) Essential Statistical Analysis
(Lecture 2) Basic Probability Models
(Lecture 3) Advanced Probability Models
(Lecture 4) Tests of Hypotheses
(Lecture 5) Analysis of Variance
Student Learning Experience
Students are expected to learn basic concepts and theories from 5 two-hour lectures for 5 weeks. In five hours tutorial sessions, they will learn how to apply the basic concepts and theories learned in the lectures to solve statistical problems. Problem solving skills will be developed through completing their assignments.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | For MSc Business Analytics students, or by permission of course organiser. Please contact the course secretary. |
Course Delivery Information
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| Academic year 2019/20, Not available to visiting students (SS1)
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Quota: 47 |
| Course Start |
Block 1 (Sem 1) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Seminar/Tutorial Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
81 )
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| Assessment (Further Info) |
Written Exam
60 %,
Coursework
40 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Group Assignment (40% weighting)
Assesses Learning Outcomes 1 to 5.
The group assignment consists of group work on analysing a set of data. The assessment is broken down into 3 submissions (3 of them in total) consisting of 1) data exploration and research question formulation 2) methodology to answer research questions and 3) conclusions, recommendations and limitations.
Individual Written Examination (60% weighting)
Assesses Learning Outcomes 1 and 3.
The written examination will focus on quantitative analyses through inference testing, statistics and probability theory. It will be a 2-hour examination and will be assessed individually. |
| Feedback |
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. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S1 (December) | CMSE11432 Principles of Data Analytics | 2:00 | |
Learning Outcomes
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
- 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
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Reading List
| Basic Business Statistics: Concepts and Applications (by David M. Levine, Timothy C. Krehbiel, Mark L. Berenson) |
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 such as Minitab and SPSS
-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.
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| Keywords | Not entered |
Contacts
| Course organiser | Dr Aakil Caunhye
Tel:
Email: Aakil.Caunhye@ed.ac.uk |
Course secretary | Miss Lauren Millson
Tel: (0131 6)51 3013
Email: Lauren.Millson@ed.ac.uk |
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