Avis et commentaires pour d'étudiants pour Capstone: Retrieving, Processing, and Visualizing Data with Python par Université du Michigan

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非常棒！

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I liked the idea of doing a capstone project to bring together all of the skills of the previous courses. This course is pretty good and Dr. Chuck's teaching style is a plus as always. But there were a few reasons why I was a little less enthusiastic than the previous courses of this specialization. For this capstone, the videos aren't all that helpful when the assignments require few changes to the scripts that are readily available. The most I learned was when I was working on a project or question that I was interested in. If I got stuck, then I would then go back and watch the videos in closer detail where I got stuck and/or code in the examples. That would solidify the lessons a little bit more in my head. The quiz format in the earlier courses was great but it wasn't included here, partly because of the format. What could be done in the capstone is to have snippets of code that are missing then ask us to fill it in. Alternatively, you can have different snippets of code and ask us which is correct to properly execute a section. In addition, I thought we would get to present our coding work on the project of interest, even if it was just optional. Sharing our work would have allowed for a shared sense of accomplishment with others who finished the specialization. Group gratification can be a good motivator. In the end, though, I really enjoyed this specialization and enjoyed completing the courses.

I wanted to mention that this last course in the specialization, despite being as great as the other four, has a huge number of typos. I took screenshots of some of them which I can share at nicolas.posunko@gmail.com. I'll try to list those I noticed since I can't paste the screenshots here:

— Week 7 > Visualizing new Data Sources - Introduction: This week you will discuss the analysis of your data *to the class*.

— Final test: Which of the following lines will never print out regardless *fo* the value of "x"?

— Final test: To *insure* that all Python reserved words are properly spelled.

— Week 4 > Loading and Modeling Mail Data: *This assignment you will start* the spidering of …

— Week 4 > Mailing List Data - Part I: … and it *make* take a few days to get graded.

— Week 5 > README: Analyzing an EMAIL Archive from gmane and *vizualizing* the data …

— Week 6 > Visualizing Email Data: … we must *be* remember that not all …

— Week 6 > Visualizing Email Data: This is also a good time *to remember to remind* those with slow …

The capstone project should be more involving in terms of coding in python. Mostly I do is to run the same files in the same order to parse the same information which Dr. Chuck has already extracted in the videos and doing all the steps same. It is lagging the novelty from the student part. There should a section where we got to write some part of the code to perform certain steps, may be code correction, may be changing some steps to bring out a different output. Although the teaching style of Dr. Chuck is commendable and so is the course material.

Sorry to say, but this should be the best part of the specialization, but a lot of things are really covered very fast. I didn't like the scheme where you could earn your certificate from the first week just by answering the questions from the previous parts. A lot of people have this certificate as a motivation to make something extra and move forward. Of course, there is a lot of useful materials on this course, but as I said earlier the level of their coverage is not a level of Dr. Chuck.

If you have done the exercise from the book in Chapter 16 you pretty much have done the capstone, apart from the additional optional ones. It would have been better if this would have been new exercises as this really would create some focus on all the previous materials. Still I feel it is worth doing as this is the final piece in the Python for Everybody course. Overall it has been a fun and great experience, so thank you Dr Chuck!

Course materials were ok for me, just giving a working program and executing. But grading needs to be improved. I think all automatic grading or all peer grading are better. It looked like that there are too many students compared to the grader and the grader could not grade on time. Maybe the grader should grade only seriously flawed ones reported by peers. I had to pay another 49$ due to the late grading.

While the information provided in the course is great, I think it lacks exercises from the student side. It is hard to truly understand the material (and understand what you do not understand) until you actually have to solve the problem yourself (in this case, write the code).

Also, Charles Severance is a great teacher. I hope that he will introduce more advanced courses about programming into the coursera.

I LOVED the content of this class, this is absolutely what I enjoy about Python and what I want to use it for. I wish, however, that there was more actual coding to do, instead of just running pre-written programs. The assignments were fairly fast and easy to gloss over - I think a bit more hands on and required assignments would help give a better working knowledge of the content.

This course is for everyone put into practice what it has been learned during the four previous courses in the Specialization. It is really necessary to have a background on python in order to acomplish it, even though the tasks are not really complicated. However, previous knowledge is necessary for understanding what is taught.

Thanks to all the staff for the support.

I still think that there is a lot to learn. the fact that I have limited time to complete the tasks made me hurry and not dig deeper into things I would like to.

If I could use the forum and ask questions after the course ended it would be better.

generally the course is very good for beginners. The instructor is very good and encouraged me to continue learning.

Good for beginners to get a feel of how data retrieval, processing, and visualization can be accomplished with Python. Difficult for beginners to really do data analysis on their own. Advanced courses with more coding assignments (where you have to write the code, not just execute it) would be more helpful in actually turning understanding into usable skills.