Aug 27, 2017
Quite challenging but also quite a sense of accomplishment when you finish the course. I learned a lot and think this was the course I preferred of the entire specialization. I highly recommend it!
May 04, 2019
Lectures are very good with a perfect explanation. More than lectures I liked the assignment questions. They are worth doing. You will get to know the basic foundation of text mining. :-)
By Oliverio J S J•
Feb 13, 2018
This course provides an interesting introduction to natural language processing in Python. The lessons are well thought, they are brief and to the point. It is very exciting to discover all the tools at our disposal to work in this field. The main problem of the course, as it seems to happen in the whole specialization, is resolving the assignments. Usually, they are poorly described, which forces the student to review the forums to understand what they are asked to do. In addition, the part of the tasks related to the course's topic is usually very simple, sometimes trivial. On the other hand, several hours may be required to generate the specific data structures required by the autograder an dealing with weird issues, that is, much more time is devoted to deal with autograder problems than learning about the subject. I do not understand why this problem keeps repeating one course after another.
By Ryan D•
Aug 06, 2019
I have been working through the entire specialization, Applied Data Science with Python. The first two courses of this specialization had a lot of attention to detail, the assignments were well laid out and challenging, and the addition resources linked by the instructor were really helpful. Moreover, the lectures themselves were more engaging and segmented.
This course was less informative than the other courses I've taken in this specialization. You would be much better off purchasing the O'Reilly Text Analysis in Python book and reading through it in more detail prior to taking this course or in-between lectures.
By Mandeep G•
Mar 17, 2020
After first 2 courses in this specialization this was a real disappointment. The course felt rushed and mostly dealt with how. It was real short on content related to application and why things are being done. Even the assignments didn't provide clarity on how the results are to be interpreted and what could be ther real world implications.
Material needs to be expanded to ensure that this course is not just to show how python can be used for text mining but also to include examples of where and how this is useful.
By Matt K•
Apr 17, 2019
The overall material was good. That being said, this is the first time I have taken a MOOC course and felt like 90% of the time I spent was fighting with the auto grader. The instructions in many instances were unclear, so when you are dealing with a grading system that grades items as 100% correct, vs 100% incorrect with really no feedback as to what you did wrong it can be very frustrating. Without the Discussion forums there is no way I would have ever figured out what to do for some parts of assignments.
May 03, 2019
Out of the 5 courses in the specialization, this course was not up to the level of the other courses. Full of theory not much practical explanations and there wasn't much practice modules in each week just like other modules.
For Assignments, i was not even able to refer any module in this course to check for syntaxes. It was very tough for me to solve assignments as there was no reference in the videos or practice modules.
Need huge improvements in the course.
By Massimo A•
Oct 03, 2017
Course packed of information and topics in four weeks so it feels sometimes rushed.
Especially the forth week (topic modelling, information extraction, semantic similarity and generative models all in one week) feels disconnected from the rest .
The exercises do not help too much, with several mistakes and ambiguity.
Nevertheless, the theme is really interesting. Possibly the errors can be corrected in the next runs.
Plus for using Python and NLTK.
By Chris M•
Aug 26, 2017
Content in the course is interesting and given the amount of data stored in text very valuable. However, I would encourage the staff to provide more coding examples. I would also suggest moving away from assignments and towards projects - (a) projects would likely force more comprehension instead of code shopping and (b) the autograder is terrible: I can't believe the amount of time I wasted because the autograder was not set up properly.
By Mark H•
Jan 28, 2018
I was disappointed by the lectures in this course. My impression is that extremely complex concepts are mentioned in passsing and poorly explained, while a large amount of time is spent on trivial examples. The programming assignments are more interesting and appropriately challenging (compared to other courses in the specialization), but leave me without any confidence that I could accomplish a text mining task in python independently.
By Dan B•
Jan 04, 2018
It's really unacceptable that there should be errors with the autograder (which were left unfixed) and I wasted a lot of time trying to debug code which was actually working. As well this course did a good job with the introduction to the concepts in the first two weeks and then dropped the ball with content that appears rushed and disorganized. The LDA and other concepts need to be presented better.
By Pascal R•
Oct 08, 2017
First Coursera course I've taken with mistakes in the material and the grader. Also the first course where they mostly decided not to provide notebooks to review the material but instead made you scrub through the videos to find the actual code. Lastly the assignments were not terribly well tuned to the lectures (which were decent) and didn't make me feel like I had a great grasp of the material.
By Yonatan S•
Nov 15, 2019
A lot of exercises have unclear instructions (see discussion forums). The exercise on topic modeling especially was a waste of time, you're not really learning anything by running these small pre-frabricated scripts. In general the exercises were extremely shallow and did not require any creativity or actual problem-solving, in contrast to some of the earlier courses in this Specialization.
By Bruce H•
Jul 30, 2018
One of the more disappointing classes in the U of M data science specialization, due mostly to inconsistent quality of the assignments. The videos are interesting but lacking in detail. The quizzes are trivial. Half of the assignments were OK but the other two were big time-wasters. The construction of this class seems just plain lazy. Proceed directly to google and skip this class.
By Casey D T•
Oct 20, 2018
This course was not particularly well put together. I found the erratic behavior of the autograder for assignments to be a significant barrier to learning. This course was far more about battling data structures and python libraries than it was about text mining. The word "Applied" in the title should be replaced with "VERY VERY APPLIED..."
By Steven G•
Nov 06, 2019
Confusing explanations of NLP concepts. Inadequate explanations of how to use the Python packages to solve the assignment questions. I'm writing this review half-way through the Applied Social Networks Analysis course which is excellent and pitched just right. The contrast between the 2 courses couldn't be greater.
By Gonzalo P•
May 14, 2019
Los ejercicios de este curso tienen una dificultad muy superior a lo mostrado durante las clases, lo que hace que uno deba de invertir mucho tiempo en los mismos investigando en recursos internos. Por ejemplo, con una dedicatoria semanal de 10-15 horas me llevó 2 meses enteros hacer el curso.
By Saravanan C•
Aug 12, 2017
Liked the simplified content. But minimalist approach w.r.to coverage of concepts - could be better. Tactical/Operational support, responsiveness from the TA w.r.to confusions on questions or grader can significantly improve. Thanks for the course, I learnt and enjoyed the hands-on sessions.
By li m•
Oct 28, 2018
I am kind of disappointed of this course especially the lecturer were talking too much than showing the practical examples, for example 'topic modelling'. With a few slides of introduction about topic modelling showing some lines of code without any examples in notebook isn't helping a lot.
By Yohann W•
Apr 27, 2020
Disappointing compared to the other courses of this specialisation. Some concepts were not defined (i.e bag of words) for the assignment. A lot of errors in auto graders, assignments. I had the impression to have a list of concepts and functions without a real explanation.
By Raul M•
Jun 02, 2018
I didn't like too much the structure of the lecture and the assignments, I don't think they were aligned that well. Also, I'm not sure how I'm going use this in real life.
The additional lectures were TOO MUCH theory which is not the purpose of the specialization.
Aug 25, 2017
Very disappointing course. Probably cause I have learnt text mining from other specialization, does not feel this course is necessary to take. Assignment material are poorly prepared, waste some time when completing the assignment, which can be avoided.
By Jared H•
Feb 27, 2020
Poorly constructed course overall. Covers some key topics so may still be worthwhile but lectures and assignments do not match up, expectation is that you just Google the material to complete assignments. Could do that without signing up for a course.
By Lin Y•
Jul 16, 2019
This course is probably the worst amongst all other courses in this specialization. The term 'applied' in the course title makes you think that this course helps you to build practical experiences in text mining. However, not true at all.
By Fabrice L•
Aug 10, 2017
This course repeat a lot what we have seen in the module 3 of the specialization. There is not enough coding examples and the first assignment is not well design. The lectures doesn't prepare you enough to tackle the assignments.
By Vincenzo T•
Apr 27, 2019
Course is very interesting. However, getting your assignments right is extremely annoying. Sometimes you have no idea why it's not right. Every upload you need to change the type of your upload.
By Goh S T•
Jun 16, 2020
course materials are minimal and possible insufficient to complete assignments without additional reading materials. Assignment questions can be clearer with sample output will be very helpful.