Coursera: Applied Text Mining in Python Review

Applied Text Mining in Python is the 4th course in the 5-part Applied Data Science with Python Specialization offered by the University of Michigan on Coursera. The 4-week course covers a variety of topics related to working with text data in Python, including built in string functions, regular expressions, natural language processing with the NLTK package, text classification and topic modeling. You need to know how to program in Python to take the course, but you don't really need to have taken the 3 lead up courses in the specialization to take this course. Grading is based on weekly quizzes and programming assignments, but similar to the 2nd course in the specialization, all assignments are locked to students who do not pay for the full certificate.

Course content consists of roughly 35 to 60 minutes of video lecture per week, including a few short programming demonstrations where you can follow along with a programming notebook and see how to use and implement some of the methods discussed in lecture. The lecture and instruction quality are adequate, although individual lectures are a bit longer than is typical for modern MOOCs with many lectures running in the 15-20 minute range instead of being broken down into smaller segments. The course covers a good range of topics although certain important text mining subjects like bag of words and term frequency–inverse document frequency are only mentioned briefly in demonstrations, while a lot of time is devoted to explaining Naive Bayes and SVMs which are not specific to text mining and were already covered in the 3rd course in the specialization.

Applied Text Mining in Python is a good first course on text analysis in Python, but locked assignments mean freeware students will miss out on a lot of the course's value. It doesn't make a lot of sense that half the courses in this specialization have open assignments and half of them have locked assignments. It would be better if they were consistent throughout.

I give Applied Text Mining in Python 3 out of 5 stars: Okay. 

Note: I might rate the course higher if I had access to the programming assignments; this rating is from the perspective of a freeware student.