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.
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Course Delivery Information
| Not being delivered |
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)
Resource List:
https://eu01.alma.exlibrisgroup.com/leganto/public/44UOE_INST/lists/26573727850002466?auth=SAML |
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 text normalisation to analysing results in detail
3. describe the natural language processing techniques in detail
4. show a thorough understanding of 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 modelling
2. be able to describe the various use cases of text 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. load text data from various sources such as local and remote files
2. analyse text-based data with Python
D. Generic Cognitive Skills:
1. demonstrate report writing skills;
2. demonstrate presentation skills;
3. demonstrate business understanding and problem-solving skills |
| Keywords | Not entered |
Contacts
| Course organiser | Dr André Santos
Tel: (0131 6)50 8074
Email: Andre.Santos@ed.ac.uk |
Course secretary | Ms Emily Davis
Tel: (0131 6)51 7112
Email: Emily.Davis@ed.ac.uk |
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