Text Retrieval and Search Engines

Text Retrieval and Search Engines is the second course in Coursera's new data mining specialization offered by the University of Illinois at Urbana-Champaign. The course covers a variety of topics in text data mining and natural language processing including text retrieval, query ranking and evaluation methods, methods and the basics of recommender systems. Grading is based entirely on 4 weekly quizzes comprised of 10 multiple choice questions. You only get 1 attempt on the quizzes.

The weekly content in Text Retrieval and Search Engines consists of around 10 video lectures that range from 5 to 20 minutes followed by a short 10 question quiz. If that sounds like a lot of lecture per question, it is, and there are no in-lecture quizzes to reinforce concepts as you go along. The lectures themselves are definitely a step up from the first course in the specialization, Pattern Discovery in Data Mining. The professor isn't hard to understand this time around and he explains concepts well enough to grasp them without having to re-watch videos. As with many of Coursera's other 4-week specializations, however, lectures sometimes turn into information dumps where the professor ends up reading off slides. The course does have a C++ programming assignment which was nice to see.

Text Retrieval and Search Engines is a decent course that is worth a look if you are interested in text data mining and search engines. Although the lectures lackluster, they have some good information. If you're planning on getting a verified certificate, it is a good idea to try the practice quizzes before submitting the real one.

I give this course 2.75 out of 5 stars: Fair.

I've taken a number of courses on Coursera and have thoroughly enjoyed some of them, but it's clear that the quality varies. I was very disappointed in this course. Having applied to the University of Illinois' Master of Computer Science - Data Science, I thought it'd be a good idea to take some of their Coursera courses to get a sense of the quality of their education. I probably should have taken their classes first and then applied, saving me the trouble. If this is the type of instruction I can expect in the Masters program, I think I'll save myself the $19k in tuition.

The problems I have with this course are as follows:

- The quizzes for weeks 1 and 4 do not cover the material learned during those weeks. I've pointed it out on their forum and others have pointed it out in their reviews of the course on Coursera. This has not been fixed so they're not maintaining the course.
- I wanted to do the programming exercises to *really* learn something, not just go through the motions. I tried installing the required software, MeTA on two different Windows computers (W7 & W10) and it wouldn't install on either one. I was not the only one with the same problem but never found a solution. I then got a Linux VM and installed it successfully on there, only to be unable to install the UofI code required for the first assignment. Again, I was not the only one with this problem. For both issues, I posted on the forum. None of the "moderators" or instructors ever responded.
- The instructor was difficult to understand at first. Once you've listened to him for a bit, it gets easier though, so it was only a problem for me during the first couple of videos.
- I thought the instructor took too much time explaining some of the obvious things and too little time explaining the more complex things. More examples would have been very helpful.

I would not recommend paying to take this course as the quizzes aren't particularly useful and you most likely won't be able to get the programming assignments to work. I think this course is emblematic of one of the issues I see on Coursera: too much of a reliance on "peers" to help you.

I was initially excited for this course as it seemed a good dive into unstructured text data. But now I'd say: *skip this course*. I think the instructor is okay and presents the material in a sufficient enough manner to get a decent grasp of it.

The reason I'd say skip this course is that the exercises are pretty bad. The class is only graded on quizzes and the optional programming assignments use an obscure text mining/analysis tool called MeTA which is time consuming to setup unless you're experienced in navigating the mess that open source C++ libraries are. Once you've set it up, it basically just runs you through a set of contrived steps that don't require any much programming or critical thinking.

To ACTUALLY learn document ranking and text retrieval, you really should have to get your hands dirty in constructing code that will do this, preferably on real world data or a very interesting test data set. And this course does not offer anything near this. I will complete this course, but only because I paid for it.

Thus I don't think most students will get more than a surface-level glance of text retrieval and search-engine-construction & document retrieval from this course.

And thus I'd say skip this course and find a better one. And to the instructors I'd say, add new programming assignments that require students to implement their own systems. Step-step handholding is no way to learn. Also, use tools that are more universal to the data science world.