good for an junior data analyst and have to be nice internship for the people who have opted for this course and to get it easily when they have to complete it in less time also. They have a lot of case study being learning is an important factor for this. And then by clicking submit you must agree feedback may be viewed by the coursera community for the compliance of the profile privacy is being done. junior data analyst and have to be nice internship for the people who have opted for this course and to get it easily when they have to complete it in less time also. They have a lot of case study being learning is an important factor for this. And then by clicking submit you must agree feedback may be viewed by the coursera community for the compliance of the profile privacy is being done.

There is always room for improvement. I do think this course needs to be updated. Requirements need to be more clear. There needs to be better visual examples and links to helpful hints. Communicating in the forums seems arduous, and getting responses with well constructed sentences was next to impossible. This is not a criticism, but an observation. One thing I really wished that was available was a link you could click if you got stuck and you could see better examples of what to do. Having a button that says, "If you are stuck, click here". I know the objective was to see things in new ways, and think for myself...and I often felt that a little more guidance was warranted.

Anyhow, thanks for making this possible for me to learn something new and exciting.

Overall, I enjoyed the learning experience. I did find that the data files were much larger than what we had used in the past, which created problems when it came to uploading them into BigQuery and Google Sheets. I was able to make do, but in ways that weren't ideal in terms of making use of the tools we had learned throughout the other courses. I wound up performing all of my analysis in Excel.

I did feel like the interview advice and simulations were helpful, and I appreciated the reminder about the pipeline of companies hiring out of the course completion, which I had forgotten about.

I enjoyed the content this programme offered and the online practice was convenient too. There was one thing I would suggest Coursera and Google to improve which is the online test. There were a couple of times when I tried to redo the test, the page couldn't be refreshed successfully and therefore in the end I had to wait for 24 hrs due to repetation. And also, when doing the programming or SQL related tests, the pages sometimes couldn't run properly so I was confused that if or not I entered the wrong answers but in the end my original answers were correct but the page didn't work.

Overall I think it could have been a big harder - I was actually disappointed that no one would be looking at my capstone project and grading it. And I'm slightly annoyed that the capstone project I chose had data sizing issues that exceeded the systems I was supposed to use to analyze it - I wasted a lot of time trying to make things work when eventually they didn't. But otherwise the rest of the course was fine. Most of the module topics I was already familiar with. The R Studio content could have been swapped around a bit for better understanding. But overall it was very helpful.

The dataset used for divvy bike was too large for platforms like R. That was the most difficult part of the project. Rstudio would crash multiple times anytime a month that had a lot of entries were used. I was able to complete the coding but after many attempts. I think for the purpose of the capstone maybe using less months of the data would be more useful. I think the amount of data has increased since the beginning of this course which may be why the issue didn't exist before. Otherwise the project was good and definitely had me sharpen my r programming skills.

The module featuring the Tableau tool could have dealt with the topic in much more detail. Could have shared a bunch of examples ranging from easier ones to the tougher ones with at least the scripts for adding new columns and including and manipulating the visual elements documented and shared. Some challenging problems or an optional section to try out some challenging codes could have been added. Also, notifications to remind people to at least spend 15 minutes with the course would be awesome.

Great beginning introductory Course! Although, employers desire numerous other skills besides what is offred with this google certificate. More coaching needs to be available in order for a student to become proficient in the course work provided. I would be willing to pay more for one and one coaching that would help me to succeed. I felt numerous HR professionals are setting the bar too high (time goals) in order to receive people who might be proficient in the course work provided. Thanks

The capstone is quite a great exercise to go through what I've learned in months. I strongly encourage Google to add more capstone-like exercise in each course so that students can have a down-to-earth case studies and solutions. The only weakness of this course is that the datasets and the question of capstone are not articulated enough, as well as the reference solution. Thus, It took me time to guess what exactly the definition of the data column and what can I do beyond the reference guide.

Not bad. I finished in 3 months. You do get an intro into SQL, R, sheets, tableau, etc. Check out some other options, though. Perhaps think beyond the certificate you earn at the end. You can learn much more productively with slightly less guidance and more personal initiative. I.e. print out the R(programming) guides straight from CRAN and learn that way instead of watching a bunch of tokenistic video tutorials. Just an example. For the price, it's worth it.

Informative and very helpful. However I thought that more instructions and info should of been given when covering coding languages. Also more insight on how things are set up and work, in some platforms, should of been given. I took notes on literally everything but felt unprepared when the capstone project came my way. Definetily will need to reach out to other resources whilst I continue to code. That said, a lot was covered and I am happy.

The reason I am given a less star is only because, I wanted a feedback or one-to-one video feedback with expertise about how I did the capstone project, so that I could apply better efforts in future. I also noticed doing this course didn't help me much in getting work opportunities. The best part were the instructors. I also liked the mock interview conducted for understanding of better approach in regards to interview and resume.

This course was a strange and wonderful mix of technology and art. This is a great start for anyone interersted in Data Analytics. That said, the technical content on SQL and R is very introductory and very repetitive - it doesn't build enough, and, honestly, doesn't give a solid enough foundation to tackle the capstone. You will have to do a significant amount of learning on your own to be able to complete a worthwhile capstone.

Overall it is a great program, they teach you the basics to get an understanding of what data analytics is. One issue is that they introduce a program early on and then they don't come back to it until the end. There are refreshers but you would think it can all be done at one time. It is also easy to get lost between SQL and R and remembering all syntaxes. Great introduction course and great for learning at your pace.

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I will keep my review short and direct, I learned a lot of new things as a beginner in Data. The course is designed for those who have no knowledge or experience in data in general. Some courses are basically "common sense" and I wish I did not spend that much time on them, but they might be helpful for other people. SQL and R are amazing and I needed other resources to learn more.

I think that this course was great for introducing the concepts of data analytics. I do believe that more hands on work starting from the beginning - with a step by step approach to building up a complete case study- would be beneficial. Completing that and then starting an independent case study would give one a firm basis for completing the steps of an independent case study.

Everything about the Google Data Analytics Certificate program was excellent—it's truly been a great learning experience. The hands-on projects and case studies were particularly valuable. Looking back, I would suggest considering Python as an alternative to R for future data analysis work, as it offers a wide range of libraries and is highly versatile.

A lot of information that can be helpful or disorientating. I enjoyed the challenge while I was working on it. I would have like more ways to practice the skills without step-by-step instructions. Raw data and answers where we had to find our own way to those answers. Trying to make up my own problems to practice with was outside of my capability.

The training is great but some how incomplete. It would be greatest if the training included some statistics fundamental concepts like variable types, measurement scale, distributions, descriptive stats and hypothesis testing, source of data (primary and secondary), best practice of primary data collection. The capstone project is Awesome

When taking the tests, there were times where I would answer a question right and it would be marked wrong. When I retook the test again and provided the same answer as before, it said it was right. All and all, I have learned a lot and I do feel confident in what I know. If I can say anything, it would be thank you Coursera.

Definitely a great refresher of the many fundamentals of data analysis. Additionally, I enjoyed touching on popular tools, because it allows future analysts to prepare for the job market. One thing I wish this course had was more learning on data analysis using unstructured data like audio and video files.

Its good! I think if you have zero experience, this is a great starter, but there will be much more to do after this. I personally never took any type of data analysis course, which is why I wanted to do this, and I think it did give me a better sense of what that type of work really looks and feels like.

Cap stone project is always a great idea! I liked the fact that there are 2 ready to use projects to use from and guidelines how to setup your own project. I missed some more detailed step-by-step instructions how to continue with the project (but in the end you can wrap your head around it).

The course is good for the beginner of data analytics but a bit rough. It requires students to further learn the skill set after taking the course. Overall, it is a great course to let people know more about data analytics and provide the overview of the industry and skills set to learn.