Students will tell you that this is a difficult but useful course — possibly the most difficult course in the KM programme. I think students find the course difficult for 2 reasons:
- Other courses in the KM programme are management oriented, so students are not used to the hands-on, practical skills nature of the course. We have continued to maintain the course in the KM programme because faculty think it is important for students to have some practical skills — to be more attractive to employers.
- Students are mentally lazy at the end of the day, and don’t want to think very hard! (Sorry!)
This course does not require a lot of reading. However there are many weird counter-intuitive concepts to learn, and new ways of thinking about data. Students have to mentally wrestle with new data analysis concepts week after week — until they have a headache. Students tell me that, often, they think they understand a concept when I explain it in class, but find that they can’t remember or understand it later. That’s the nature of the subject. One has to grapple with each concept 4 or 5 times before it becomes familiar and commonsensical! My advice to students is to review lecture material right after class (on the train home), and again on the way to class the following week. I usually spend the first half hour of each class reviewing the previous week’s material.
Students also complain that they feel lost in the first half of the semester (when they’re learning statistical analysis). That is expected — “no pain, no gain”! I’ve scheduled a mid-term so that students can consolidate what they’ve learnt and have a sense of attainment after the mid-term. Most students do get there and become competent in data analysis and pass the course — but they do complain a lot along the way!
Actually, such is the life of professional data miners. To be successful in data mining you have to immerse yourself in the data, and wrestle with the data all the time — to find that useful pattern or business idea that will help your organisation.
Prerequisite for the course: Students must have taken at least 1 semester of statistical analysis at the undergraduate level, and be comfortable analyzing data using a spreadsheet programme. (This is on advice of previous students–so that the concepts covered in the first few weeks are at least faintly familiar you.)
