Course Review – Data Mining Capstone (UIUC)

Here is my review of Aug-Oct 2015 Coursera incarnation of Data Mining Capstone, which is the final project in Data Mining specialization. To be enrolled, students need to pass all other courses in specialization. I passed the Capstone with 91.5% grade being ranked 4th out of 186 on the competition leaderboard.

Technologies/Material: In contrast to theoretical courses in Data Mining specialization, this course has only applied part – students are expected to solve a research problem and write a report every (!) week + write a 10+ pages final report. The course is very demanding and one can only pass having prior experience in academic research/data mining. The techniques exercised for research problems are topic mining, comparative text mining, clustering, supervised contextual learning, and machine learning. The base dataset is that for Yelp data challenge. Learners are free to choose their own programming language, since only the report and the produced knowledge are graded. The suggested solutions paths are sometimes ineffective and provide just the first step to achieve the results, e.g. suggested sentiment analysis algorithm underperforms and is not usable, suggested selection of small numbers of reviews hinders efficient topic modeling, it is unclear that comparative text analysis is the only way to reliably compare similar datasets.

Instructor/lectures: The course has no lectures, but only instructions on how to complete the research assignments + grading rubric. User guides, tutorials, and reading materials are provided for the suggested data mining tools, e.g. TopMine and SegPhrase. Peer review is employed for grading.

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