There are a lot of inconsistencies here. The quality of the pedagogy is generally abysmal to merely lackluster, production values on some of the videos are very low (I'm looking at you, series of poorly organized screencasts with mumbled and heavily accented, rambling narration). The final certificate says the course is three weeks of work when it's actually four. In the end, I passed with full credit but other than the guides to connect Jupyter to GitHub and then Watson Studio to Github it was all by dint of what I knew before I entered the course and not because of anything I learned in it. But it is useful to have those tools connected so it's worth doing that. I find it hard to believe this is 1 of 4 courses in one of the most highly-reviewed specializations for Data Science, though. Hard to believe and disappointing.

Good introduction to programming languages and useful development environments. However, the IBM Watson section felt like a long commercial for IBM products. That section makes you jump through a number of hoops to register for IBM Cloud, a service that requires a credit card to even register if you don't follow the free trial instructions that are only given after the video lesson telling you to sign up. If you follow the video instructions you can lock yourself out of getting a free trial because your email can only be assigned to one account. IBM/Coursera's advice if this happens is "use another email address". Beyond that the instructions for completing exercises using that tool are out of date and not generally correct. This one section was enough to almost make quit this whole course, really disappointing.

I found the material difficult only because the videos were unorganized, often having multiple speakers in one video. Week 3 was a complete mess. I could not complete the lab exercises because IBM suspended my account (that had only just been created), but I'm not sure I could have followed along anyway because the videos did not match up to what the website looked like. Questions for the quizzes and final exam were extremely confusing, and I had to make multiple attempts to pass. The material just would not stick. I wish it had been presented more clearly. I am not sure of the relevance of the material, or how/where to use it. Weeks 1 and 2 seemed okay, but Week 3 as I said just went out the window. I was looking forward to learning more about the tools, but I still feel quite lost.

There are lots of technical glitches in this one. Quizzes in a video before you've gotten to that information, missing links, places that ask your thoughts on something that really doesn't need thoughts, etc. I think it's also misplaced in the series. It assumes that you know a lot more about data science than you do if you've just watched "What is Data Science?" They talk about some great resources for writing in Python, R and Scala, but you haven't yet learned them if you take this as the second class in the series. I think there's probably a lot I didn't pick up on, simply because I didn't have the base that knowledge needed to build on. It might be a great class (if they fixed the glitches), if placed elsewhere in the series.

They brand it "Introductory" but there is little introduction. Instead, it serves at best as a refresher of topics for someone who used to know these a while back. It is NOT for the beginner as the mother course would have you believe. Going into specifics: Module 1 must be the dullest most boring class I've ever taken. the AI speech enumerates 50+ open-source and commercial tools in data science and we are supposed to learn them by heart? What is the point? Module 4 rushes you into R and GitHub: it packs so much content and gives so few explanation; instead, it again enumerates concepts and attributes as if they're a grocery shopping list for a wedding of 300. The final exam has 2 broken questions that make them unsolvable.

I did not enjoy my time taking this course. There is simply TOO MUCH information, and it rarely gets expounded upon. I often felt like I was just getting information dumped on me, such as listing the various Python or R packages we MIGHT use one day. Additionally, videos would often use data sets as examples but would fail to explain their context and don't seem to provide the data sets to work with on our own. This wasn't the case for the entire course. I enjoyed Week 3 where we spent a significant amount of time on one thing, such as jupyter notebooks or github. Instead of being told how something worked, we were able to play with these tools ourselves, and as a result I feel that I learned SO much more.

In the Data Science Tools Chapter the content was poorly structured and I felt overwhelmed by the tool names thrown at me with no structure in the presentation. I even found that the tools given as examples are not actually relevant now and there are a lot more "popular" commercial tools that were not mentioned once in the chapter ex. PowerBI, Google Data Studio, AWS Redshift, Google Big Query. And the amount of times IBM Watson Studio had to be repeated in the course is pretty annoying. Like we already know what it can do, you don't have it repeat it 10 times. I think the content focused way too much on the IBM tool. You should've named this course IBM Tools for Data Science instead.

This course leaves you doubting whether you can become a data scientist rather than providing you with brief knowledge on data science tools. I think it needs quite a bit of restructuring because concepts are explained in videos as if they are being explained to computer scientists and engineers rather than newcomers to the world of data science. Nevertheless, I'm glad I was able to get through it and move on to other courses in the certification that actually matter. My advice for you: don't worry about the complexity of the material and don't waste your time trying to understand the details because it's simply not worth it. Just get a grasp of the essentials and move on.

A good overview of tools that are common to the field. The videos are just plain horrible. The narrator does not explain anything that he is typing on the screen, he makes frequent mistakes, and parts of the screen are not even visible. One video shot showed the narrator recording the video from the driver's seat of his car! Quality needs more focus. Also, the explanation of some of the labs are confusing and many many many students encountering similar errors...but when looking at that discussion forum, staff just cut-and-paste same unhelpful message to everyone. I guess you either figured it all out and make it through the course, or give up in frustration.

Part for Git was really great. It has provided me good overview what it is and how I'm supposed to leverage it at basic level, so it was very useful. Part about Jupyter Note was also good, part for R was alright, mainly because I'm not much interested in R anymore. But then too much time has to be spent for IBM tools. I understand that these courses are constructed by IBM and they would like to leverage it to advertise their tools. However, from learners perspective (presumably most of us are just beginner in this field), IBM tool part was just an overkill, way too much. I probably will prefer those courses provided by universities more moving forward.

It was interesting at the beginning but along the way it became uninterestingly long and monotonous. Though the part explaining the IBM opening part was hard to follow as my free trial had expired and there were no more claim codes for the extended trial. Most of the reference points and labs were from outdated versions. So they didn't load as expected. I experienced multiple error messages while trying to open the IBM cloud account and navigating to the Jupyter notebooks. The IBM page was also glitchy. I was not able to get access to the extended trial claim code so I had to open a new account to do the lab works and exercises.

Outdated lectures and low quality videos. Owful monotonous narration (puts you to sleep). It is as if IBM & Coursera are trying on purpose to put people off Data Science. :-)

The 1st course of this specialisation (What is Data Science) was truly educational, fun and easy to watch thus to understand and remember what you have learned. This course is the opposite.

IBM & Coursera people, the fact that someone is a super duper scientist doesn't automatically qualify to be a teacher nor a narrator, and please IBM, do improve the quality of the videos with better graphics and enriched material. We know that you can afford to do so.

This course had several issues. First it was too much information for someone who has no background knowledge and therefore difficult to understand all the terms used without any examples to relate. Second, there were several audio related issues that were not resolved. I wasnt able to access IBM Watson Studio and even though I reported the issue I ended up losing marks on an assignment that required IBM Watson Studio which was no fault of mine. There were unclear instructions, graded assignments included content not taught the lab work (it wasn't mandatory to study the cheat sheet). Overall, disappointed.

There are some very helpful parts of this course, such as those on using GitHub that helped me tremendously and were very easy to understand. That being said, there is so much of an emphasis on pushing IBM's products that it seriously hamstrings the course. Conditioning a point of your final grade on submitting through IBM Watson is a very silly thing when data science is such an open field with a multiplicity of tools. I understand this is an IBM course, but it's a cheap and slimy thing to make so much of the course basically an advertisement for IBM products. Our time is scarce; please respect it.

The first course in the IBM Data Science Professional track was excellent. This one is not.

This particular course is dry compared to the previous one. It’s difficult to pay attention when many of the videos are a boring presentation of dozens of data science tools. Week two is less dry but is poorly organized and needs more explanation and refinement (poor audio quality, lacking descriptions). There are also videos which are essentially repeats (cover the same information) of the previous course but of worse quality. Overall, this course lacks refinement and organization, and is not worth paying for.

The content is a good intro into the tools that you are gonna use in a data career. However, I did have two major issues here where:

1) As IBM moved to Watson Studio from Data Science Experience, this did create some of the visuals and knowledge a bit outdated.

2) There was a massive disconnect when you have issues working Watson; especially with signing up and logging in. I see that this was a huge issue since then (judging by reading the discussion sections of the course) and this (along with my first point0 should definitely be discussed if you want this content to still provided.

It is a very quickly course, the week of the open source tools it's seen really quick, you can barely understand anything and they put three different tools in one week, if you really want to learn them search another course; it is also a big propaganda for de IBM tools, and they put so much more effort on selling you the idea that their products are really necessary that in actually introduce you to the tools that a data scientist use, and its so hard to understand if you don't speak english as your mother language, hope that with time they can fix it.

This course is not good. It has taken me several times the amount of time specified due to the outdated videos that no longer represent the tool as provided. Struggling to get simple markdown commands to work in the notebook, but I successfully executed them in the previous toolset. Several back and forth's between the actual work and the videos is very aggravating. It would be nice if you at least provided some printable instructions. I feel that I have gotten off track and may not be able to find my way back to a normalized starting point.

Very patchy. Some of the videos were quite informative but the practical exercises were a real problem. Often the instructions were impossible to follow as the software must be a newer version than that shown in the instructions. I had to Google for the latest instructions. Also the practical exercises rarely told you *why* you were learning anything; it said "type in this code" but you didn't understand the code. I was able to finish the course but only through sheer perseverance (and Googling for instructions) - it was not enjoyable.

This course at one time would have been great. However, for myself and many others (based on numerous discussion posts during my time taking the course), there was a multitude of technical errors and outdated training. Zeppelin Notebooks was not a working functionality in the IBM Skills Network, and most of the walkthrough guides were unhelpful due to updates in the tools being showcased. The course is in need of some serious updating to catch up on many changes that have impacted the tools covered in this course.

Material in this course looked like part of something else. It's a beginner's course but suddently I'm faced with a lot of technical jargon with no explanation. I supposed to experiment with software and code I've never seen or know how to interpret and I'm just suppose to run it to see what happens. I have no idea what I was supposed to learn from it. Week 2 was demotivating and added nothing to my knowledge. This week was an absolute loss of time. I still learned something from week 1 and 3

give one plus star with benefits of the doubt. If the course gets better in next courses but this is a shame. I can deal you promote IBM services and products, no matter with that, but you only give ALL the tools existing in the data science world and meanings, but with no knowledge is a none sense. Hope you improve, Basic stuff for analytics, statical or regression models, clustering I have no clue what they are. But well the name is tools for data science. No how to use them or what for.

The course needs to be updated. Especially the IBM Watson Studio section is something which needs to be worked on again. The site they refer to has hanged completely. IBM site was not user friendly and caused a lot of confusion. It took a lot of time to figure it out. This problem is not isolated and there have been a lot of users who are struggling to figure out how to solve it. This causes additional problems as the final assignment depends on getting started with the IBM website.

A lot of contents are not accurate since IBM website has been changed . And the final exam was terrible. For example, one answer was like Both A and B and there were no indication for A and B. Another question:What type of model would you use if you wanted to find the relationship between dependent and independent variables? If you choose regression model, it's wrong. If you choose Classification model, wrong again. Do I suppose to choose the wrong answer to get 100% grade?

A very fast-paced course with a lot of non-sense material for beginners. There is redundancy in final exam instructions; it is difficult to know which one to follow. Some videos have poor audio quality. There are too many links here and there that don't make sense. Many of the instructions for lab work are based on the older version of IBM studio, making it difficult to locate the buttons or tabs referenced in the videos and texts. IBM! You've already let me down!