Learner Reviews & Feedback for SQL for Data Science Capstone Project by University of California, Davis

I really don't understand why this course is added to this specialization. It is not really linked to the previous courses, and the way to get the grades are all based on peer-reviewed assignment. There is no peers to review my work after submitting the first assignment and there are four such assignment to go.

It was Ok. It's largely down to you to get the project done to the highest standard you can muster... if you're stuck just use stackoverflow. But through that I learned a hell of a lot, just stick to your ambition with answering your question and plug away at trying to get the answer even if it means googling lots to work lots of stuff out yourself. If you do, you may have a project worthy of being shared with your network or even blogged about.

Some feedback: given this was part of a SQL specialization, it would've been interesting to ensure candidates created a database that they would query themselves, rather than just draw the ER diagram. But to be fair on Course 4, that wasn't taught in the courses before, just seems to me to be an opportunity missed?

Another feedback: it may have been more helpful for the examples to be based in Spark / Pyspark for them to have the feel they build on Course 3 of this Specialization. Again, a missed opportunity if people just reverted to Pandas and not use the Capstone to apply Course 3 teachings.

But as always and perhaps rightly it's up to the learner to construct their project in a way that's meaningful to their learning goals.

Thanks for the course

the course is just an assignment, without much guidance and student is supposed to capitalize on whatever they have learned in previous courses. the requirement to use Python is frustrating, it would have been better if the course would have remained focus on SQL. limited people are enrolled in course, it takes weeks for peer review.

I think the aim for the course encourage exploration using the learned skill. Which is fine, actually a great idea. But the execution is sloppy. I think the course need to be balanced between exploration and steering as well. It should be designed so that some insights was hidden in the data and with no particular instruction, the students should find out these insights. So they have meaningful goal that they reached as well as a handful of exploration experience from the lack of instruction.

Instead? We have 3 sets of data, which are too narrow even compare to the example given in the lecture. Also the lecture and the assignments are barely comparable, which is more confusing. The peer review is cute and all but not too useful in this kind of capstone project where learners are generally inexperience.

Overall, not too much value was added from the course unless you want to experience the frustration of working with data, which you can get elsewhere. Not recommended

The content and direction were above and beyond what I was expecting. This is a real class- I spent at least 160 hours on the specialization and have legitimately upgraded my SQL skills to intermediate. The structure of the classes cannot be overstated, as each module builds on each other and the cumulative projects are done in chunks each class (albeit with the last one an exception, there was less hand-holding there and more focus on where we can go from here (Jupyter, Python, Tableau, things like that). If you're out of work due to the pandemic, there is literally no better way to spend your time than learning the skills needed today, from this class. Can't say enough.

thank you for this course. It has been very interesting to do it :)

Me gustó la experiencia práctica y el proceso de desarrollar un ejercicio sencillo de minería de datos desde la importación de los datos hasta la presentación de gráficos de manera autónoma. Lo que no me gustó fué que en la 4ta semana la mayoría del contenido eran links a otro curso de Tableau, lo cual le resta valor agregado a este curso. Por otro lado, no es necesario saber python para hacer las tareas pero el 80% del contenido de las clases son en python. Aún así aprendí varias cosas en el proceso y pude aplicar la librería Scikit-learn para hacer unas regresiones con los datos proveídos.

Some support is missing, and they should also enable sending pdfs on all deliveries (every week)

It was good well structured course.

The concept of this course should be appreciated. It is very nice to conduct a project by yourself to understand better what you've learned. Indeed, the course planed is organized so that you can go ahead step by step to complete your project. However, I need to put negative points to technical problems. You see something interesting in the videos, but its codes are not provided and there is no explanation of how you can reproduce what you see in the videos...

The final course was difficult to follow. The instructor was using a different application than what was taught in previous courses. It was very difficult to see his screen. He was only slightly teaching the Jupyter application he was using, but not with depth. He was more giving ideas of what we could do with our own projects. It would have been better to actually learn more. But I enjoyed the opportunity to practice more and complete a presentation.

Not enough guidance on how to do the assignments- lots of use of python when there is no background of it in previous courses. The videos skimmed through it as well.