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This is my first course in Coursera. I appreciated the high level views of data analytics and review of many of the processes and tools used therein. I found a couple of the quiz questions to be a bit subjective, referencing specific speakers' views on the topic. But, this is not enough to not recommend this course. The final exam is a guided exercise in data analysis that offers a great balance of giving the student a challenge while also making it attainable. This exam is also peer reviewed. I did not like that one of my responses was marked down because it did not exactly match the key of "some of" the possible correct answers, when I believe I either fully met or exceeded the requirements for full credit on that question. Again, the course is still worth taking.

It was a nice course to assist an individual in the more "philosophical," and conceptual nature of Data Analytics. Which is good for a beginners, so it was a good course. After the "Week 1" series of lectures and quizzes, I noted between quiz attempts that the questions stayed the same.

I would recommend changing up the question bank between graded and practice quiz attempts as it then requires someone to pay close attention to the lectures and engage themselves in the method(s) to best help themself learn. Even the conceptual topics can help tie together a subject matter and provide a learner with a solid foundation in understanding future topics/areas of study.

This is a great course that can be completed in about 2 weeks if you're working about 4 hours per day. It is a good overview of Data Analytics and the tools/knowledge required to do the job. I took away one star because I think that the course would be easier to absorb if the slides better supported the information that is being conveyed. The slides are in complete sentences instead of bullet pointed so it requires the student to stop after each slide and create bullet points to match the verbiage from the video or else right down word for word what the instructor is saying (which is a great deal). Otherwise a great course that goes very quickly.

I enjoyed this course, and there would be two things that would make the experience better. 1. Provide formative practice opportunities during the short videos. Several new terms and concepts were introduced, and breaking them up and providing opportunities for practice before the quiz at the end of each week would be helpful. Second, the peer review section was great to review answers, but from a UX perspective, it wasn't easy to differentiate the student's work vs the rubric. It would be helpful to make it clearer by using bold or highlights in the box.

Decent content but the quizzes/graded material could use significant improvement. Questions don't test knowledge of the concept but often reference minutia that could be easily looked up later. For example, I don't need to remember the exact Excel functions in the "Function Library". In some cases, I had to reference my notes or Google the answer because I didn't memorize all details of the course. The peer reviewed material isn't a great idea. If your answer isn't in the format provided in the rubric, "peers" will just mark your answer wrong.

The course is structured very well and really helps to understand what it is about Data Analytics and if it's the right fit for the student. I gave 4 stars only because of the peer-graded assignment. Initially, a failed only because the peer just gave random points when I did everything properly and then the second time I got 9/10 points, again not 10/10 only because the peer didn't read my full answer I think, all of this without leaving any feedback. This can affect the student and can decrease his motivation for continuing with the course.

I learned the introduction to data analytics and now I have an idea of what data analitics is.

The narrative was too artificial and made me bored and hard to focus on. I had to replay a couple of times. It would have been great if it included more real cases.

It's hard to understand or remember the tools with just plain text without visual examples. It spends lots of time explaining tools and I even think if that was necessary.

I like the advise videos from real data analytics.

Everything was clear until the language of the final project where "points" were used to mean data columns, which was confusing. The course talks about the importance of story telling for presenting data analysis. It could use more stories/examples to help with the dense amount of new vocabulary. Learning the names of programs used for different parts of data analysis is good for increasing recognition, but doesn't have much to make it stick until you start actually using them.

A very good introduction to Data analytics .. if someone is new to this field then this is the perfect course for them to understand what is data analytics, what are the skillset required to progress in this field and how the data analytics actually works in an organization using various software and tools. It also has the complete detail of list of technologies used for Data analysis.

Taking further coursers of IBM will help to develop the skills mentioned in this course.

It is a great opportunity for all data analyst enthusiast to start their journey. The content of this course is excellent. But some beginning modules lack of interaction. They are like boring university classes. Try to make them interactive. More industry experts' opinion need to include. Try to record some live screen record videos instead of traditional slides. But overall, I am satisfied. Thank you IBM, thank you Coursera.

Overall, it was a good introduction talking about lots of different things. Most information felt relevant. Sometimes they expected us to remember the individual benefits of different software, even though we have no tangible experience with any of them. I don't really feel it's helpful to know off the top of my head how they each program compares with each other on what felt like a very specific level.