Muy poco practico

très bon cours

Very Math!

good

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 ..."

This course will throw a lot of information at you despite how short the instructional videos are. I found myself referring to the various Python library documentation sites just to get a better understanding of the formulas and concepts being introduced.

The lab modules leave a lot to be desired as well. Most of the time, you are just clicking through until you get to the bottom of the notebook. Also, please be warned that some of the functions taught in this class will not work as they have been updated in the last few years.

I am disappointed this class was a struggle to get through and I hope future students have a better experience.

Seems more adequate for people who have a background in statistical analysis. The labs are confusing and there is no orientation to the tool being used so it has taken me quite a while to figure out how to even proceed through a lab. After spending considerable time doing the lab, it may not submit the results and Coursera assumes you haven't take it yet which means you have to do it all over again. Other courses I've taken are structured much more clearly, step-by-step, providing activities that allow you to gain confidence before throwing you off the deep end. This one could use the help of an instructional design expert.

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.

Not a very good course. The information given in the videos was not explained well and key concepts seemed to be brushed past. The graded assignments were very dumbed down and did not reflect the difficulty of the videos. This was quite lucky though as the videos were not very good either. It seems like the graded assignments were dumbed down so that the course could actually be completed without further background reading.

More information should be added, longer length videos, and get rid of the peer-review system. Lazy.

I would not classify this as a BEGINNER course that only requires Python and Jupyter Notebooks knowledge as advertised. Statistical knowledge should be recommended before enrolling in this course. In one video, they just threw out acronyms like SLR and MLR before these were even explained. In addition, with the amount of plots that they made us do in this course whose syntax are very unfamilar, I feel that should be after the Data Visualization Python course in the Data Science track.

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.

Thanks Coursera and IBM for providing this condition for all Enthusiasts.

In my opinion, in this course, Python did not learn in the best possible way.

I'm very sorry that your website does not verify Iranian users,

Your goal is to provide education for everyone,

Why should we, who are interested in education, be influenced by politics?

Isn't education the best way to help deprived countries?

I feel that this policy has no place in educational justice.

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.

Bad final exams, little to none explanation of information, lazy test and text writing. I was not interested in this course, I did not manage to learn much due to lack of theory and explanation why do I need to count these scores, what do I achieve with them and how do I choose methods. I feel like authors of this course are well educated on theme but did not care much to explain it to new learners

Course content not explained properly. Instructor introduced the topic and very less explanation on the topic provided. I have to study the topics with external help to gain the proper understanding. I would suggest to the course designers to redesign the course content with emphasis on explaining the concepts. All the topics covered under this course are lacking on explanation part.

Excelente curso, muy pedagógico, ilustrado, teoría y práctica hacen posible el aprendizaje. El feedback es fundamental y no lo proporcionan. Los script deberían aparecer al inicio para ir desarrollando el proceso, los codigos están desactualizados, hay funciones que no están vigentes en Python, librerías y funciones desactualizadas retrasan el avance, optimización y resultados.

Course starts off great up till and including module 3. From Module 4 (model dev) course gets a bit hectic and needs to be broken down smaller sections with more explaining. Important difficult concepts are packed into modules 4 and 5 more time and explaining is required here and quite honestly i have processed nothing from those modules 4 & 5 and i redid the modules twice.

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.