The breakdown of the grading policy for the course is shown below. The contribution (in terms of percentages) of each component is as follows:
|Class Participation / Attendance
|Group Proj. Proposal
|Group Proj. Presentation
|Group Proj. Final Report
There is no final examination for this course. The final examination component will be replaced with a Group Final Project Report and Presentation.
Class Participation (10%):
Student’s attendance and participation in class discussions will be considered in the grading for class participation.
Individual Assignment (15%):
Students are expected to complete an individual assignment in the first half of the semester to gauge their understanding of the course materials so as to prepare them for the Midterm Examinations and their Group Project. Details of the individual assignment will be announced later.
Midterm Examination (25%):
Students are expected to attend the Midterm Examination. It will be open-book and open-notes. It will focus on the application of the materials covered in class. The mode of the delivery of the examination will be determined and announced later.
Group Project (50%):
You are required to form a project group with 4-5 members. Your project task is to apply the web/data mining and textual machine learning techniques that you have acquired to gain insights and draw interesting conclusions to a (business) problem. You are to apply (advanced) data mining and analytics tools (preferably in Python as Python tools are used as supplementary aids during the delivery of this course) to process structured and unstructured text available on the Web. You will then summarize your insights and present your conclusions using suitable visual aids.
The Group Project will consist of three parts: (I) Group Project Proposal (10%); (II) Group Project Final Report (15%); (III) Group Project Final Presentation (25%). In general, the project is to include a discussion on a real-world problem, data collection and cleaning methods, suitable textual machine learning techniques that can be applied and the insights gained after performing text analytics.
More detailed instructions and the guidelines for this course project are given in BT5153 Project Guidelines and Grading Criteria.pdf (could be found in Workbin/Lecture Notes/).