The videos are not updated. I know the people in the texts say so but it was very difficult to me to follow one of them. Due to this, I realize how bad is Coursera's help and support and forum management since I had to find my way out to finish the course. This is a complaint on the platform, not on the course. The course is OK.

The instructions on IBM Watson studio aren't very good, given that the tool has changed. Spent over an hour trying to create a new Jupyter Notebook. During the storage selection, after clicking 'Add', you need to pick existing and not Create New. Then select the storage, go back and click 'Refresh' on the Notebook creation page.

Rather uneven. Some parts are explained step-by-step, some are vaguely glossed over. Several times, course material is mentioned, either in the labs or (worse) in the quizzes, that hasn't been seen yet. Some technical problems with Watson Studio have yet to be solved. Overall, the tools are presented without useful context.

About half of the lessons are useless, as the lecturer jumps into topics that never came up before and starts coding stuff, without any explanation. He seems to be a good data scientist, but lecturing people is not his thing. There are hundreds of complaints in the forum about these lessons, but nothing was updated so far.

The overall course was pretty good. It felt a little bloated. Final Exam instructions were unclear. The pop out instructions that said "Detailed Assignment Instructions" Did not list what had to be in it. Which meant the first time around I didn't have enough because there was no clear guidance until you read the rubric.

Good overview but doesn't dive into anything specific. It might be more useful to have a limited number of tools in the overview (like the ones most commonly used or what would be used when starting) and go into slightly more detail on their uses and applications. Nice to finally work with GitHuh and Jupyter Notebooks.

Links and screenshots are badly in need of being updated. I had to find my owns way to sign up for IBM Watson, and it still took over 24 hrs for me to get my login verified. Then when you are finally signed up, the video tutorial isn't relevant because it's not the latest version of IBM Watson Studio being discussed.

Die ganzen IBM speziellen Tools halte ich für unnötig. Es hat ein bisschen den Eindruck der Werbung für die IBM Produkte. Wer wirklich Interesse an den Produkten hat, der wird sich von selbst darüber informieren. In diesem Kurs sind die Videos uninteressant.

Der erste Teil des Kurses mit Python und R ist gut gemacht.

Awesome content, but I (and many other students) had problems with IBM's Watson license, which is an essencial part of this course and all the rest of this specialization. I had to create a second account with another e-mail and now they're asking me to provide my credit card info and use a pay-as-you-go license.

The course was good although I felt that some elements which were difficult to grasp could have been elaborated more. Also, the videos are I assume old, so the actual site which is now IBM Watson has completely changed, so it became difficult initially to get to it. But, it got great when the actual work started.

Es un buen curso de introducción para conocer herramientas y lenguajes de programación relacionados con la ciencia de datos. Considero este curso como esencial para poder identificar que herramientas están a nuestro alcance y con cuales es mas factible trabajar como principiantes, en esta fascinante disciplina.

The general introduction could be useful for beginner who is new into the tech industry. I would recommend to look at the sections first before start. If you know them already, please skip as it doesn't not provide anything new. In addition, why do I have to pay subscription fee to watch IBM training videos?

Decent overview of the open source tools used in Watson Studio. I would have liked a more hands-on approach, but considering that this course comes after the "What is Data Science?" introductory course, it fits the purpose of broadly understanding what Jupyter Notebooks, Zeppelin Notebooks and RStudio is.

I changed my mind. This course is not so bad. I don't rate it 4-5 because I don't like the fact that the interface in IBM Watson Studio is not the same as the current one which makes it a little bit confusing and because some of the videos have audio issues and introduce some concepts without explantion.

In this one, I am giving a 3 star, not because of the course content. But because of the way it is delivered.

Without a proper introduction to various things such as Git or GitHub, codes are thrown at the screen suddenly. And it is confusing. Though a proper reading material comes at a much later stage.

Instructions are not very clear at all and almost impossible to follow. Things took a lot longer than they should have just to get to where you needed to be. The work required was not difficult, but the lack of instructions on how to navigate and get where you needed to be was extremely frustrating.

Learning about the frameworks and apps was nice. Watson Cloud is kinda cool. However, mentioning too many names in one video was sort of challenging and IBM tools part was sooo overwhelming (especially SPSS video was like taken from an advance tutorial and has nearly zero annotations on the visuals)

Week 2 was not very well produced. The IBM produced videos were very well done. Had to do some self-service poling around to get information that should have been presented in the course work. Each time I visit the IBM Watson studio it is a new exploration to get started on assignments/projects.

The tools are at a very very high level and covers so much on machine learning. A beginner needs to learn about the basics of Data science before taking up a course on the tools. Otherwise it will not be of much use. It covers lots of machine learning terms which a beginner might not aware of.

Course has a lot of information which is outdated from the tools. This should be updated at least by a text file. Also note that the test tool does not work with a computer running linux which also should be informed.

Else the topic was great and the course was really interesting and compelling.

It is great to see what tools are used for data science but some explaination on how particural nodes in the workflow work (from the mathematical/alghorithmical point of view) would be even better. The final exam was rather about learning things by heart than actually understanding the topics.

The videos have no sense of continuity. There is also a section where the video lecturer who is showing us how to use jupyter notebooks is rushing through the tutorial and even gives a video lecture from his car. Not as professional as I would like or expect for something of Coursera's calibre

Average. The peer reviewed and screenshot-taking is a mistake, just get rid of it. Apart from that, the R Studio section is badly structured. You either explain the syntax and funcion of the commands or don't even bother asking people to type them in since they have no idea what is going on.

Some videos were unclear. For example, the one where teacher were sitting in the car. I could feel that he was in a rush at the moment, and has to make a course at the same time. In some other videos repeated the same problem - speech was unclear and not all things were properly explained.

I got some problems with R Studio with https://labs.cognitiveclass.ai/ it stops several times so I decided to use the IDE with IBM cloud. The lesson was a little bit too much easy for me and a little bit boring maybe next time next time the course could use only tools present in IBM cloud.