Coursera - Text Mining and Analytics Review

Text Mining and Analytics is the fourth course in the Data Mining specialization offered by the University of Illinois at Urbana-Champagne through Coursera. Text Mining builds upon the second course in the specialization, Text Retrieval and Search Engines. Course topics include mining word relations, topic discovery, text clustering, text categorization and sentiment analysis. The course lists programming proficiency (especially in C++) and knowledge of probability and statistics. Keeping with the system established by other data mining specialization track courses, grading is based entirely upon 4 multiple choice quizzes with 10 questions apiece. You only get one attempt at the quizzes.


Text Mining and Analytics is information-packed. Each week has 2.5 to 4 hours of lecture content in video segments that generally range from 10 to 20 minutes. The videos quality is satisfactory but the explanations and content on the slides could be a bit clearer. Despite the long videos, there are no comprehension questions or exercises to interact with during or after lecture segments to reinforce learning. By the time you reach the quiz at the end of the unit, you may find yourself having to go back review certain videos to answer the questions. There is an optional programming assignment.


Text Mining and Analytics covers many useful data mining topics, but it has too much lackluster video content for its own good. I can’t help but feel like a better course would have been able to condense the videos down to cover the same topics in half the time, leaving room for more quizzes and exercises. This course could serve as useful as reference material but students watching straight through may find a lot of information going in one ear and out the other.


I give Text Mining and Analytics 2.5 out of 5 stars: Mediocre.