Final assignment is quite messy

Not clear on the last part

This course is very tough

Lots of technical issues

Exams should be harder

la lala la la laa aaa

Not easy to follow.

Very Math!

Begins relatively clear. The practice labs were coherent and straightforward.

Around Week 4, things started to get convoluted. Small things, things that you don't notice at first.

Week 5 was where it really started to fall apart. You could tell whoever made this course lost interest or just did not have the capacity to teach the information effectively.

A great example of the lack of understanding or knowledge of how Coursera works is something you can view yourself.

Week 6 is the Final Project

Week 7 is one statement about your certificate.

Usually in most courses, the final project will be in end of the final week. That week having multiple modules that you have to complete leading up to the final. It was worrying for me as I thought the approach to this was on accident, but it seems likely that it was just due to ignorance.

Just as well, the Final Project was botched, the software and questions were depreciated and even written wrong by the creator. And when you would upload your pictures in the end to show you had worked out the problem, one of the upload buttons was missing in lieu of the letter "Y"....

Y indeed. Y was the ending of this course so terrible? A little more investment in the people you are teaching would go a long way. Very disappointed.

Definitely not one of my favorite courses in the Data Science Certificate series. There were times I was ready to give up the pursuit of the certificate altogether during this class... There should have been a prerequisite for this course of the statistical tools and methods that would be covered in here... Sure I could program these things after this class, but i still dont understand why I would choose to use one over another? This is one of those classes where you walk away feeling more confused than when you went in... Also there were a lot of mistakes, typos, and obsolete things in the labwork - some reported and acknowledged months ago, but still not fixed in the lab (video I can understand, but not the labs)

The content is good, but if you are not familiar with Python, I wouldn´t recommend this course. There are a lot of typos in the video. The code contains a lot of errors where you have to find a solution. So, you are forced to debug their code often.

But if you are only interested in the course certificate, you could quickly go through the videos and quizzes, without any problems. It's easy to pass because the questions are like: What is the result of print("Hello world"). So no real challenges at all.

Please, try to fix the typos. Sometimes it was very embarrassing. Example (Week 3) instead of

"from sklearn.metrics ..." the video comes up with "from sklearn.metrixs ..."

many typos, errors, mislabeled... just felt like a sloppy product were paying for. I was very frustrated as well by certain features not functioning... for example, after following specif instructions to share a notebook, just as I have done many times while working on this certification... testing the link comes back as unshared no matter what I do. This and the SQL course have been the worst so far in this Data Science cert but at least this course ended up marked as completed. If I wasn't already this far invested in the cert I would definitely quit and use free resources while I built my portfolio.

-1- The training and quizzes are full of errors. You need someone to actually review the content before publishing.

-2- The education focused more on the mechanics of how to run certain commands to obtain results rather than explaining why a data scientist would want to run these certain commands and how to best interpret them.

-3- I would embed more but perhaps smaller lab assignments rather than going over many concepts and making the person go through the steps (with minimal explanation) at the end of the module. This is particularly applicable for weeks 4 & 5.

There were so many typos and errors about the very topics they were teaching. It is as if they don't actually care that people are trying to learn this and just view this course as a way to promote their Watson Studio. Normally I would forgive these errors, but there are programmers so paying attention to detail is paramount. Also, misspelling method names while you are teaching those very methods and then never showing how to spell them again makes for some serious confusion.

The final peer graded assignment has considerable coding issues. Regplot does not execute in the Watson Studio despite proper coding. During submission one question does not have the arrangement to upload JPEG file for submission so all you can do is post the code. The Q8 is dropped out of the blue with no reference availabe in any of the courses.

The course itself is very informative but it is very evident that no one is reviewing whether everything is working properly.

Unfortunately it's a bit rushed and the statistics module should be expanded and taken apart. The scripts in some "on-screen" quiz don't work and no matter what you type it just doesn't go wel. The quizzes are really hard and the whole module should slow down, and take the most important subjects and develop them. This is, ofc, a crash course and you can't expect more than this, but so far, it's the only downside to a brilliant professional course.

This course is not friendly to new beginners in Python. Especially the weeks 3-5 are too intensive without any real explanations of the logic behind the code shown. Linear Regression, ridge regression, etc are too advanced for new joiners who struggle with basic python. Also, there are some erroneous slides present in a couple of videos that add to the confusion. Would not recommend this course to any Python beginners.

This course was riddled with errors, it was honestly really hard to follow. It's also extremely frustrating that the errors were pointed out by others for a really long time (several months for some), and none of it were fixed.

The subject matter was also extremely difficult to follow and the explanations provided were insufficient for beginners.

The final assignment code had some major issues. I kept getting the error 'NaN, Infinity, or big data type' whenever I tried to compute inspite of my code being right (I cross checked my code with my peers assignment as well). The videos miss out several critical bits of information. This course was a very frustrating experience for me overall.

Course started out alright but towards the end if became more about simply plugging in data into imported functions without a deep understanding as to the underlying mechanics. The part about pipelines was especially rushed.

Labs should provide new examples for students to work on instead of asking students to slightly modify code.

The devaluation of this specialization course reached the lowest when ADVANCED STATISTICS was introduced to all students without MENTINIONING in the course outline. It should have been ONLY for ANALHYZING the dats , instead of trying to cover whole of modeling and evaluation in a few slides which is never enough.

DISAPPOINTED.

Worst course I’ve ever taken on Coursera. It starts off ok, but quickly goes downhill. Many concepts very quickly or poorly explained; lab assignments filled with typos and errors and in some cases not connected to content in videos. I finished the course, but it was painful and I didn’t learn much. Very disappointed.

Rather poor way to get hands on learning. The "lab" does not offer an effective way to learn. This course was a poor substitute for a real instructor. Also, the last two weeks' material became more complicated but the information supplied to learn it did not increase nor provide clear or different explanations.

The course itself 5 star. But because the lab experience is so terrible, I minus 3 stars. Please fix or remove Cognitiveclass completely from future courses, it delays my learning and interrupt my focus on the subject, there are 3 more courses to go and I can only expect more bad lab experience.

This course let me down. The crux of real-world is in analysis and in this course the author, IMHO, didn't do justice in explaining the concepts, the why are things done the way they are clearly; instead the author opted to breeze through things.