The instructors are pretty boring, however, even though the course has good content, the resources are buggy, there are too many mistakes in the assessments and not even IBM recognizes the course.

You might as well learn via youtube

I learned so much from this course about Data analysis Process. I gain skills Like, Python Language and Libraries, SQL, Advance excel and Data visualization tools. I got job a junior level data analyst job.

The course is good, thank you. But I have some recommendations to the final assignment (presentation). The task is not clear, it is very difficult to understand what exactly we should do. Do we need to use all data of the Stack Overflow’s annual Developer Survey or just only technology trends? And in submission section there are questions and its not clear if we need to put all information from the slides there or just write -"Yes", 'No'. I think authors should write better instruction for assignment. And you should mention that grades depend on the number of findings/conclusions/results in the presentation.

IBM software bugs out way too often and renders some of the course material impossible to complete until their system repairs itself.

This course was filled with things we did not previously cover in other modules, and then had very little or no correlation to the final capstone. The final capstone project assignment itself was great, but the labs and quizzes leading up to it felt like a large waste of time.

I give 1 star to this course as it needs improvements. The project is rather chaotic and there are many errors in it. It would be good to work with the same dataset/file throughout the entire course. Big drawback is that the free plan in IBM Cloud is limited. In my case, it came to an end at the very beginning of this project. Thus I had to find and use another tool for Jupiter Notebooks and Python. In the end I didn't manage to do all tasks with CDE as I simply didn't have it even though I created a new account to use IMB Watson Studio.

There is so much wrong with this course. 1. After using Jupyter Notebook for the past 5 courses, you are now forced to use Watson Studio. There are little to no instructions on how to use the tool, there is a limit to how much data you can use (you run out around week 4) and when you search the discussion forums, the help you receive is minimal. Any instruction you get are outdated screenshots. 2. The activities in the course do not align with the tests. In the first week I had to take a test and answer questions about a lab that came after completing the test. I only found that out because I couldn't find the data set (which they provided in a completely different course). 3. What you do in the labs do not align with the questions you are supposed to answer in the quizzes. In week two you will be asked how many duplicate rows there are for the column, 'Respondent' except nowhere in the lab does it answer the question and you don't have access to the dataset. 4. There is too much useless information. In week one there is a whole section about job openings at Github with provided links. The links bring you to a 404 page and nowhere in the section do you use that information. It is a waste of space and time to make student jump through hoops and confuse them with useless and redundant information. I was enrolled in the IBM Data Analyst certification and unfortunately this was the last course. The above errors were not limited to just this course but the whole certification.

Very poor course, the software is not working like Cognos, always mistakes

The skills and tools taught are interesting and useful, and it's rewarding to build your own project within the guiding structure of the course. That said, the curriculum was often vague, requiring considerable time just to divine what was being asked for. Support from the teaching staff in the forums seemed inconsistent, with many questions receiving unhelpful responses and some simply ignored. Of course, struggling with the material pushes you to develop a deeper understanding of it, and the open-endedness is good practice for real-world problems. Ultimately, I was able to figure it out, but I think a few corrections and clarifications would improve the curriculum.

Overall, I found the course to be great. The Capstone project itself was a little frustrating. We were expected to know subjects we hadn't covered yet, the labs were riddled with errors, and some of the source material is outdated. Please take a look into this.

Disappointed in this course, could be much better if more work is done. IBM software and oversights in design were my biggest issues. Some parts are intuitive and well paced, while others are difficult for a person without programming experience. I expected better from a course with IBM's name on it.

In this last module, I completed some Jupyter exercises and when I took the test for the section I had to return to the Jupyter lab project and add in more code to answer additional questions. Just have me do those exercises in the first place, then I can answer all the questions and it won't waste my time going back and forth. The IBM software ran out of processing time on the trial when I was busy with the Python exercises, then you have to wait a full month for it to reset so I ended up having to use Jupyter running on my computer to finish before the module was due.

I learned a lot of useul skills and content was great, but I think there are things that could be done to make it a more smooth and enjoyable experience.

Do not recommend. Highly frustrating.

If you need to do this course (like I did as it was the last one out of 9 in a specialization I was doing) - DO NOT USE IBM tools they want you to. Go with "optional" labs instead (unless you are fast and furious and already experienced/100% remember everything from the courses before. Trying to use IBM tools (Watson Studio) was a complete waste of time. For example, week 1 Lab on Collecting Data Using APIs I spent great deal of time and effort to figure out how to save an excel file with my results in their cloud environment in the project itself and next day - boom - can't continue "Monthly compute usage limit reached" and all the help one can get is "The Watson studio which you have created under the Lite plan is completely free, as you are using the lite plan so you will have limited access to it. This will be refreshed every 30 days from the date you have created a service.". SO HELPFUL (I'm being sarcastic)! Had to switch to "optional" labs and spend quite a bit of time on re-writing some code bits again. Went better after switching to optional labs and making everything run locally but, of course, it initially took lots of time and effort to make everything run and to large part it was because of how the course staff is replying in forums. I see that there have been many people before me with similar problems and the answers are always somewhat fuzzy and evasive and wants to push you towards using IMB Watson or Skills Network.

In labs one suddenly had to write code never seen/explained before without any helpful examples provided.

Instructions and grading criteria for the last presentation are somewhat weird. How one would even evaluate " Use any innovative ideas you have into this presentation" or " Using your creativity, make this presentation look awesome". Thought I had done enough and still got graded down. Peer-graded assignments are frustrating as they are and not having clear criteria for evaluating others/getting evaluated by others does not make it any better.

This course was exceptional and I was able to complete it and obtain my professional IBM Data Analyst Certificate ..

Kudos to all tutors who have helped in making me understand more and more concept around Data....

The course is rife with errors and often times the project submissions and quizzes don't line up with the project so you have to go back to your jupyter notebook and do more analysis to actually finish the modules. More so, some learners seem to like to fail people for no reason at all, and when it is clear that your submission is correct. This has been brought up numerous times in the discussion forums and nothing has changed. This course was the final one of nine in the IBM Data Analyst Certificate and I'm frankly surprised at the poor quality of the course since it is from IBM. As the complexity of the courses rose, the errors also rose.

This was not an easy learning, the student must been do a hard work and high comprehension about data contains, its normalization, determinate the objetives of value , aplicar filtros, and do all the necessary in order to obtain to present outstanding results through work of final presentation. I feel that in this training I have achieved a great goal with this learning.

First five weeks are great, a good hands-on exercise on everything except the process of researching (since we're handed all necessary data in links throughout the course), which is often outside our purview anyway. One notable exception is the process of saving CSV and XLSX files in the week 1 labs on APIs and webscraping (the Github job and salary data we need to use in the final Powerpoint). A tutorial lab or section DESPERATELY needs to be added teaching us how to access these files after saving them -- the process involves setting up project access tokens and a cloud storage object, then importing a special IBM cloud library allowing one to save the files as accessible data assets, but this information is difficult to find online. It should really be laid out in a lab, both for the final project and to let us test the files we made at the time we make them.

Week 6, the Powerpoint, isn't great. There's been a lot of technical education on the processes of data cleaning and everything of that nature across these nine courses, but very little in terms of what conclusions one might draw from this data. I think the certificate could use a course on this sort of actual analysis featuring examples of some of the conclusions we might draw from other datasets, or even a couple freebies at the start of this project just to point people in the right direction. When it came to coming up with innovative ideas about the data, I found myself coming up with a bunch and wondering if they were "innovative" enough; like whether simply laying out the rankings on a particular bar chart was sufficient for a "finding" point, or whether future predictions about rankings were sufficient for an implication. Grade-wise I did fine, I just think a bit of practice earlier would help us not be stumbling around so much on the final presentation.

Lab questions and exercises are extremely vague and redundant. Some of the same questions are asked over and over in multiple labs. The course developers need to either re-write the lab questions/exercises to be much more specific, or hints and solutions need to be added like in the other labs. Also, a smaller cleaner dataset would be much easier to work with. 65,000 rows and 114 columns is too big and complex for a learning environment.

Riddled with issues

Overall this bundle of courses is one of the best to start learning data analysis, it's well structured and designed. A lot of self-learning too and not everything is given to you on a platter which is good. All components of this course together can give you sufficient skills to start an entry-level role or your own project

This is a great course. I learned so much about data science. I appreciate all the help I received. I would not have finished the course without the help of my fellow students and the staff. Thank you