Postgraduate Course: Advanced Data Modelling (CMSE11419)
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 deals with the various applications that are made possible because of the advancements in data science in the last few decades, most notably for analysing text. In the first part, dealing with text data is covered. Methodologies for using text towards various applications such as text modelling and classifications are covered. Sentiment analysis will be treated as a special case of text classification. Finally, sequential data, such as purchase sequences or website visit traces, are tackled using sequence mining and modelling techniques using neural networks. |
| Course description |
Academic Description
This course deals with the various applications that are made possible because of the advancements in data science in the last few decades, most notably for analysing text. In the first part, dealing with text data is covered. Methodologies for using text towards various applications such as text modelling and classifications are covered. Sentiment analysis will be treated as a special case of text classification. Finally, sequential data, such as purchase sequences or website visit traces, are tackled using sequence mining and modelling techniques using neural networks.
Outline Content
1. Text mining
2. Text classification
3. Sentiment analysis
4. Sequence modelling
Student Learning Experience
Weekly lectures and hands-on programming exercises in Python which enables students to implement the methodologies covered in class.
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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: None |
| Course Start |
Block 4 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Seminar/Tutorial Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
78 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Case Study Analysis (100% weighting) with a (10%) Peer Assessment moderation using WebPA.
This assesses Learning Outcomes 1 to 3.
The case study project will be performed individually or in groups of 2, depending on the size of the cohort (i.e. »15), and should contain no more than 2,500 words per student. It focuses on the analysis of a body of text gathered from various sources, such as online reviews.
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| 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. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Summarise a large body of text using big data analysis techniques
- Use text towards the prediction of topic and sentiment
- Analyse sequential data to find common sequential patterns and make predictions based on sequential features
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Reading List
| Speech and Language Processing (Jurafsky and Martin) |
Additional Information
| Graduate Attributes and Skills |
After completing this course, students should be able to:
A. Knowledge and Understanding:
1. describe the full text mining process from data gathering, over data transformation to analysing results in detail
3. describe the sequence mining process in detail
4. show a thorough understanding of deep learning techniques which are appropriate for text and sequence modelling for business problems
B. Practice: applied knowledge, skills and understanding:
1. show a thorough understanding of the application areas of text and sequence modelling
2. be able to describe the various use cases of text and sequence modelling for companies of varying sizes
3. evaluate and compare various state-of-the-art text and sequence modelling techniques for various business environments
C. Communication, ICT and numeracy skills:
1. collect text data from various sources such as web sites
2. analyse text-based data, and other ordered data such as transactions with Python
D. Generic Cognitive Skills:
1. demonstrate report writing skills;
2. demonstrate presentation skills;
3. demonstrate business understanding and problem-solving skills;
4. demonstrate awareness of group dynamics.
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| Keywords | Not entered |
Contacts
| Course organiser | Dr Johannes De Smedt
Tel: (0131 6)51 1046
Email: |
Course secretary | Miss Lauren Millson
Tel: (0131 6)51 3013
Email: |
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