This course will introduce students to various techniques to analyze unstructured data. There are two components of this course; the first focusses on mechanism of representing knowledge and second on algorithms and techniques that can be used on non-traditional data sources to perform the analysis. The course will equip students to identify and apply appropriate techniques while dealing with unstructured data.
On successful completion of the course, students should be able to:
1) Develop and implement an ontology
2) Apply techniques for reasoning under uncertainty
3) Apply the algorithms for the different techniques that work on unstructured data sources like human
experts, documents and web forums
4) Conceptualize and implement an analytics solution to a practical problem which requires
unstructured data.
Introduction, basic concepts and motivation
Knowledge Representation-Production rules and Ontology
Reasoning under uncertainty
Natural Language Processing
Text Mining
Sentiment Analysis
Web Mining
Processing large amounts of data
The coursework will consist of two (2) assignments and a project. The assignments expose students to different types of practical exercises and the project exposes students to applying their knowledge to a business problem that requires data mining and their presentation skills.
Final Written Examination (2 hours) -50%
Coursework -50%
Paper Review, Discussion and Presentation – Assignment 1(20%)
Analytics - Assignment 2 (15%)
Project (15%)
Students will be required to pass both the coursework and the final examination to pass the course.