Good

good

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Week one and two were not bad, one complaint I have with week one on two were the overwhelming amount of tools mentioned in the videos which are quizzed on. It feels more like being tested on memory. I don't think my memory is bad or anything, but I just feel like it is irrelevant, but I do understand why they're mentioned in the videos, it's just the quizzes. Other than that, week one and two were not bad.

As for week 3, wow. Week 3 was convoluted. Major problem I had with week three is that the videos are outdated. I do think Watson Studio is a great resource to use, especially if you want to work for IBM, but it was confusing to figuring out how to sign up for IBM cloud for free unless you looked at the forums beforehand. There should be a disclaimer or something to give learners a heads up to read the forums before doing anything; however I don't know if you can do that currently on Coursera, would not fault the staffs or moderators if it isn't possible to do on the platform. I've seen some learners stressed regarding to signing up and using IBM Cloud. Not only signing up on IBM Cloud was an issue, it was also the interface change that is confusing to navigate for me and others. Other than that I've got everything out of the way and managed to complete week 3 and this course without being stressed out over these problems.

Anyways, continuing on the next courses and acquire the goal to get the certificate despite the problems I had with this course.

The basic content was helpful, especially for a beginner. However, it is imperative that the videos be updated to match the current version of Watson. They now show a defunct DSX environment that is very different from the Watson environment being used. One of the videos that the course analytics labeled in a "helping" pop-up comment "Was the most visited and considered important by learners" was the confusing outdated one...no doubt this was the actual reason learners played it over and over trying to make sense of it, not because it was especially valuable for learning. Another suggestion: many of the videos or reading material used a lot of jargon and DS terms as if they assumed that learners already knew these terms. It would be helpful to have an online glossary for each course with short definitions of the terms that are thrown around casually in the early videos, and sometimes defined later in the course (or maybe will be in later courses in the certificate), but that many learners are not familiar with at this point.

The actual course content was fine, but the link they used for accessing the software rarely if ever worked. It continuously showed essential software required for the course as "coming soon". Literally hundreds of students were reporting this problem in the discussion forums, but the only instructor feedback was "clear your cache and try again". Which was a not only a solution that had no bearing on the server side cloud environment features, but in many instances made the situation worse.

My only feedback is to PLEASE PLEASE make sure the software environment is working, and to PLEASE take student comments that they cannot access the environment seriously and propose real solutions. In the event that the environment is simply not usable, please suggest other methods of working through the material- IE, how to install Jupyter and Zeppelin on our local workstations.

This course was helpful, but was too reliant on IBM tools. I realize this is an IBM course, but lots of students were unable to create a Watson account, including myself. Because of that, it was difficult to do any labs or assignments involving Watson. I requested help from the tech support e-mail and never got any help. From incomplete instructions, I had to figure out how to do my final assignment in Jupyter notebooks in Anaconda and submit through Git Hub. It took lots of extra time and I was worried I didn't do something correctly with Ananconda and Git Hub and wouldn't finish the course on time. Also, too many tools were presented in this course. Although it's helpful to know of the existence of these tools, simply describing the most common ones and letting us do a Google search to learn about others would have sufficed.

It was a pretty great course. I have learnt a lot about data science tools. However, the pacing is a little bit too fast. I feel like I am in rush, so many things to absorb for a small course. It would be better if you guys extend the course a bit longer with in depth explanation about the tools with practical labs and exercies instead of breifly explaining things within a video. Another reason i gave this a 3 stars was due to some out of date instructions & screenshots in lab exercies, especially in week 3, IBM watson studio exercies. The instructions are not clear and screenshots do not match with the current version of the studio. It took me a while to understand what the instructions want me to do. Other than that it was a great course. Hope you read my review and improve the course in future. Best of Luck....

This course is "information overload". Personally I think that the structure can do with some tweaking. The reason for this is because the information lacks context.

Week 1 and 2 were a bit daunting, but if you stick with it it does get better.

It would help if there was a resources tab with summaries of the content discussed in the videos. Also, when introducing the programming languages, they should say whether these languages are required. I spent a lot of time downloading, installing, and trying to load the required libraries to do the assignments. It was only later that I discovered that it is not necessary, and it is far quicker and more reliable to do these assignments in the free resources provided.

Overall, not as bad as the other students make out in the comments, reviews and discussions.

The course is not bad at all, on the contrary, it is very rich in information. However, a lot of the IBM additional Tools were presented very briefly, with no practical examples / labs, so I believe all that info will be forgotten very fast. The quizzes are often about memorization, while they could be more about understanding the principles behind what we're learning. For instance, a few pieces of code to be interpreted could be given, instead of the same old generic knowledge. Course is a bit too heavy on the theoretical side, but manageable nonetheless. Adding a few additional practical labs could make it more balanced.

Also, the interface has changed compared to the videos, so I used a lot the forum to help me find a workaround. I am very thankful and happy with the responsiveness on the forum.

The first two modules were good. There is a lot of useful information, difficult, but step by step it's possible to understand. The Jupyter Notebook and GitHub labs were interesting and easy to understand and follow all the steps to solve the exercises. Thanks for authors!

But module 3 was not interesting. As for me, it was like a confusion. The quality of videos, audios and screen shorts were bad: with different voices in one video, with noise sometimes, the screenshot was hard to read. The quality of video about Watson Data Refinery was bad and was so difficult to understand. I had some technical problems with Watson Studio and spent a lot of time solving them. The labs were not interesting. Only the final exam was good.

The most frustrating part was accessing IBM Watson Studio. It was difficult. That could be streamlined and made clearer. The instructions were clear, but getting into Watson Studio took a lot of time and research as the sign-on procedure has changed. If I work with Watson long enough, I'll be familiar with how it operates. Watson Studios kept opening new tabs, so it made it hard to know where I was. Additionally, since it had bounced my credit cards, I had opened several emails in an attempt to gain access. When I did get in, I had to open a couple to see which one I was granted access to. GitHub was intuitive and much more user friendly.

I wish it had gone smoother. I was very interested in learning Watson Studio. .

+It was good to get an overview of all the tools and and technologies involved with the industry at the beginning. There are just so many out there that it's nice to have some context for what everything is so I don't drown in industry specific lingo while I'm learning.

The structuring of the course was a little confusing. In particular the Week 1 Module broke everything down by open-source, commercial, and cloud tools, but then each of those sections used the same categories of tools(data management tools, visualization, etc). I think it would have been more effective to break it down by category, and the note which tools were open-source, commercial, etc. As it stands, it felt jumpy and hard to keep track of.

Content is ok although course spends way too much time in listing available tools and showing how tools developed. Instead, course should focus solely in showing the characteristics of the tools . Practice excercises are great though... maybe a bit too simple. It would be great to add more excercises and reduce the history and context.

Also the exams ate terrible!!! you should consult an education master to improve this, many questions were of thing that were barely mentioned in the training or that are purposely written in a confusing way. In any case exams dont realy measure how much you understood, but how good you are to take notes and retrieve the right data from the your notes

The course is ok but materials need to be updated to reflect latest changes to IBM tools, so that students don't waste time trying to find things while setting up and accessing tools. Also, could help to include more info with realistic examples or a few interview blips with real data scientists on when they use one tool over another- real context. LIke, why use Jupyter Notebooks over Zeppelin or IBM environment - the course addresses these at high level but I think perspectives from real people in the field what they're really using, and how it solves their day to day issues and workflows is a needed addition to the course to make it more immediately useful.

Two very important things:

1. Several links are not working. Several examples are currently unavailable for students. Pages don't load, or an error message is shown.

2. The final exam asks for students to do things that are not included in the course. I understand that students may need to do some research on their own in order to complete the final exam. This is great. But when showing how to do markdown cells for example, you can add a link to an IBM tutorial on Jupyter Notebooks (I found one on the internet) saying: "The final exam may include extra content not directly given during this course. Follow this link for a complete tutoria." That would be great.

There's a huge area of opportunity here to improve the content of the lessons, or most likely the way it is presented to the students. If you visit forums of week 1 and 2, you'll find a lot of discontent from the students. Even though the lessons content is good, the way it's introduced to the students is not the best. I'm only giving three stars to create awareness of this particular issue. In my personal opinion the course was ok, I understand it's just a glance of the several tools one can use for data science but if you're curious enough you can take enough advantage of the course's content. Great tools presented and a lot of self learning left to do.

There is plenty of information provided in the videos and as an absolute new person to data science I find the background information and tutorials helpful. The things I dislike are:

1 the quizzes are just finding facts / statements word for word from the videos.

2 there is little to no help on the discussion board and there is no one to assist you on the ibm watson prompts

3 the final quiz was graded our coding ability yet we never learned how to code in this class

4 most dislike the fact that our final assignment is peer reviewed by people are also in the course and not professionals. I dislike how my grade can be impacted by the mood of someone.

The flow is erratic, lot of random information not sure where to put it .. need to explain the process of what happens with data and why the apps are needed and what they do and how they help the process (work together) .. I heard about dozens of apps but have no clue when and if I would use them or why they are needed. I am following the course but mostly having to research what you are saying .. the language is high level or sometimes overly complicated. I only bring this up because this course said no prior experience needed there is not way a person having no IT experience will come away from these first 2 modules with an understanding

The Course was good but it could have been much better if it was based on Video tutorials. It was really difficult to understand the written instrustions for me initially as in to how to perform some of the codes which we had to execute just by reading the insturctions given in the course readings. I had to refer some of the videos from youtube to understant some of the instructions, so had been the course explained through video tutorials it would have been much better to understand the module. The module was good it was just that the study material was a bit tricky and time consuming to understand due to the absence of video explations.

I cannot give 5 stars because of the not updated materials. The IBM Watson Studio interface was completely different from the one explained in the videos and many of us had difficulties with it. Please, understand that for beginners like us, it was confusing and frustrating.

But, Technical Staff are very active in the forum and try to answer all our questions. Thank you for your help.

I have still a few concepts a bit unclear and I couldn't find the answers on the internet. My doubts are around the concepts: kernel, language and interpreter. Are they all synonims? What are the differences between them?

Hope my feedback is useful.

The course design is such that it covers a lot of breadth but very little depth. A plethora of tools are introduced but not much hands on time spent in the form of exercises (within the given four weeks). This was causing a lot of confusion, which was getting worse because of the many instructors with very short videos..

It would have been more effective if one person was to take the student thru all the topics and in longer video clips that these short 2 minutes to 10 minute.

Subject is interesting, tools are interesting, could have made the delivery process more interesting..

The labs were really helpful towards teaching you the skills. I feel that the videos could have been organized better, and that the unfamiliar terms could have been defined. I would recommend mid-video or end-of-video quizzes that test the content in the videos because I was unprepared for many of the questions in the practice quizzes even as an avid notetaker (similar to what is done in course 1 of the Introduction to Data Science Specialization). The end-of-course project is comprehensive and tests a lot of what is taught throughout the course.

Overall the course was informative. The issue is with the legitimacy of how the final project is peer graded. My first submission showed that it had failed. When I went through the comments/ criteria that someone graded me on I could tell that they just randomely clicked points. I re-submitted the same exact assignment and got a 100%. This shows me that there is no controls in place to catch people who do not take this seriously and just click boxes to grade someone quickly without actually looking at what that perosn wrote. That is disturbing.

The course introduced some interesting things especially Zeppelin notebooks which I wish I had encountered earlier. There were two main negatives to the course: 1) The videos and readings for IBM Watson Studio need some serious updating - once the account was created it was very trial and error to create a notebook. 2) Some terminology may be foreign to a beginner level student. As an example, the term "Spark" would be confusing to someone new to data science as there was little to no introduction to what it meant.