Chevron Left
Applied Data Science Capstone(으)로 돌아가기

IBM의 Applied Data Science Capstone 학습자 리뷰 및 피드백

4.7
별점
5,275개의 평가
670개의 리뷰

강좌 소개

This capstone project course will give you a taste of what data scientists go through in real life when working with data. You will learn about location data and different location data providers, such as Foursquare. You will learn how to make RESTful API calls to the Foursquare API to retrieve data about venues in different neighborhoods around the world. You will also learn how to be creative in situations where data are not readily available by scraping web data and parsing HTML code. You will utilize Python and its pandas library to manipulate data, which will help you refine your skills for exploring and analyzing data. Finally, you will be required to use the Folium library to great maps of geospatial data and to communicate your results and findings. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate....

최상위 리뷰

LD
2019년 10월 23일

Its was great experience in completing the project using all skills that we learned in the course, thanks to coursera and IBM for giving me an opportunity to update my selft and also to test my skills

SG
2020년 3월 3일

Very good capstone project. Learnt lot of insights on how to represent data through out this course.\n\nVery good starting point for ""Data Science" field. I would definitely recommend this course.

필터링 기준:

Applied Data Science Capstone의 665개 리뷰 중 26~50

교육 기관: Pawel L

2019년 7월 14일

To much focus on Foursquare API

교육 기관: Armen M

2019년 12월 8일

Just terrible , No Any Idea what to build no any suggestion what methods to use togeher

교육 기관: C. L P

2020년 9월 3일

The blind leading the blind.

Vague and confusing instructions. You are trying to teach new data scientists to do a business case analysis, but you just threw some random data and some generic methodology instructions at them. How can I demonstrate the process of coming to a 'business understanding' ? There's no client, and no goal!

I am genuinely unsure whether you consider it important to use the Seattle accident data, or whether I can use some other kind of data and solve an unrelated problem. The "good" example provided for the last week is something completely unrelated. If I choose something unrelated, too, do I have to risk a bad grade from my peers who don't understand it?

To add insult to injury, there doesn't seem to be any instructor available to answer the many, many desperate questions from learners on the forum.

The whole "peer review" thing needs to be re-through, especially when dealing with such a vague assignment. And the system of everybody spamming the forums with review requests, so you can't find any actual topics for discussion with other students, is a hot mess. And, let's face it, "peer review" is pretty useless anyway (and potentially discriminatory, by the way). If I had known the grading would be on this basis, I would never have bothered with this course.

교육 기관: Mildred O

2020년 9월 16일

It would be great if resetting deadlines wouldn't erase all work/grades previously done/achieved.

교육 기관: Pablo V V

2019년 4월 2일

more exercises, more projects.

교육 기관: Sai T S

2019년 6월 20일

If I have to say one thing about Coursera or IBM Data Science Professional Certificate course, I would say it as a Fantastic thing happened in my life, I am so happy with it, and I am not going to leave Coursera for ever.

교육 기관: TJ G

2019년 6월 20일

Very difficult to manage the scope, but it is a self-learning process. Recommend extending the Capstone course another week or two, to encourage the students to go all in on their work.

교육 기관: Ozgur U

2020년 7월 3일

I finished all he 9 courses in this specialisation. Therefore, this comment basically applies most of the 9 courses.

The video contents and the practice exercises are very good and on point. Instructors are great. However, there are serious problems with the assessment mechanism and this is the reason why I am giving a 4 start.

If you work hard on the assignments, meaning that you study and research well to understand the code, you might end up getting a low score on assignments. This is because of peer-graded assignments. Your work might be graded by someone who doesn't understand the material as much as you do, or even someone who submitted a blank file just to see others' work. You rarely get feedback for the missing marks. As a simple example, I once submitted my work and received 4/11 with no feedback. I instantly re-submitted the same work and received 9/11 from another peer.

Another problem is that you get to see the rubric only after you submit. Some assignments are not clear on what the specific expectations are. The rubric must be clear before you submit the work. Even if you try to be flexible in your solution to address the vagueness, the peers may not show the same flexibility although you do the work properly.

And finally, the biggest issue.. Plagiarism! When I say plagiarism here, I mean copying someone else's work line by line all the way. It is utterly disgusting that it is more widespread than I initially thought. Such cases have been posted multiple times in the forum. I encountered at least 2 cases of plagiarism. The only thing you can do is to flag the submission, but obviously this doesn't stop anyone. What's worse is that those people who plagiarised someone else's work line by line get to peer-grade your own work.

Assessment section of these courses is a mess and has to be seriously re-evaluated. Peer graded assignments can be accepted to a certain extend but not for assignments that require hours and hours of our effort.

교육 기관: Zoltán H

2020년 10월 1일

I enjoyed working on an open ended project, which was not the case in the remaining 8 courses in this specialisation. I was completely unprepared for some common challenges with a real life dataset and it took me a hard time to address them. On the other hand, it is hideous that only one person reviews your assignment and there are absolutely no requirements regarding the accuracy of the results. I mean who would accept a statistical model with the lowest possible accuracy in your new dream job? Anyhow, the only reason why I kept improving my project is just to show it to my potential future employer and not because of the requirements. The other funny thing is the cavalcade of "Please review my assignment" threads in the discussion forums, which makes it impossible to have a meaningful discussion there. In conclusion, I did other courses on Coursera, but this one had a far lower quality than those.

교육 기관: Garima K

2020년 3월 31일

Outdated and poorly taught specialisation. My best experience on Coursera has been Andrew Ng's ML course and maybe it raised the bar too high. But that was a course that taught the student (keyword: taught). This does not even come close. Would not recommend.

교육 기관: Dillon R

2020년 10월 14일

The course project was changed 4 days before the due date. This is unfair and it is a waste of time. If you don't want to waste 2 weeks of your time I would advise you not to take this course.

교육 기관: Ferenc F P

2019년 2월 26일

This is really challenging course, especially that you get hint on how to use a RESTful API (of Foursquare), how to create heat maps, or create different marking on a map using folium. The Capstone was really challenging, because you can practice what you have learned during the courses of the specialization, like how to start from the scratch a project, how to apply the data science methodology, like business understanding, gathering, analyzing, and cleaning data (most of your time you will spend on this), applying the right machine learning algorithm to solve the problem (modeling), using Jupyter Notebook on IBM cloud and using github. In the end you should also prepare your final report including the business understanding, describing your data, presenting your result, and placing a discussion section in the end. It took me 4 full days to complete the capstone, but I learned a lot.

교육 기관: Piyush L

2019년 10월 21일

This is the best part of the specialization and I learned a lot in this Capstone Project. If you've been doing all of the 8 previous courses, believe me those 8 courses are nothing compared to this course when it comes to putting time and hard work. You will learn a lot of things including web scraping, connecting to a url, using geolocation services to get data about a location. You'll also use foursquare API to get popular venues in a particular location. This project is super interesting but at the same you have to put in a lot of work too! It took me more than a month to do this capstone alone but it can be easily done in around 3 weeks if you're dedicated in completing it.

교육 기관: James C

2019년 7월 14일

Good class, very useful. Peer grading is a great idea, don't like the practice of posting notes in the forum with subjects like "You grade mine and I'll grade yours." At the least, it gives the appearance of cheating. It is also wasteful, as it leads to some assignments being graded multiple times while others are waiting in the queue. This is a practice that Coursera encourages, which is baffling to me. Even in the last class in a 9 class series, I ran into people submitting blank or nearly blank assignments, with no content or inappropriate content, who were apparently hoping for a pity pass or cheating.

교육 기관: Nur C N

2019년 7월 23일

This course is really good and give enough challenge on the final project, especially on how to get data from multiple sources: scraping data from web, call APIs, and visualize it on map after call the clustering algorithm. I like the way we should prepare all material to complete the course like visual presentation with slide/blog post, report, and share the code in GitHub. Really glad I take all these 9 courses, can't wait to take other specialization course.

교육 기관: LEOPOLDO S

2019년 1월 8일

Very Good. This is my first contact with data science with python and associated packages.

In the end of the course I'm able to deal with data using python and a lot of tools that helps the job and let this job more fun.

A very well organized and balanced course with videos and very good material for practical labs.

I have a Swisse Knife with me to deal with future researches on data science.

Thanks.

교육 기관: sumit g

2020년 1월 26일

The course was very helpful in presenting me the world of data science, what exactly are the things we need to be proficient in to excel in this field ! Best course of all was Machine learning with Python, you will enjoy doing it ! and we need more questions in quiz to test what student has gained at every step.

교육 기관: Naga M

2018년 12월 16일

This is a very useful capstone project in which you can apply all the learning you have done throughout the course, the more practice you do the more you learn. I like this course from coursera and recommend it for data science aspirants.

Thank You!

교육 기관: Jamiil T A

2019년 3월 30일

A must take capstone project. Enroll for it and you will be moved by the project... Very interesting !

교육 기관: Stefan A

2020년 7월 19일

To much focus on the use of the Foursquare API, which is outdated is bit. Other techniques learned in the program are not used, only clustering with K-means. On the other hand, you are forced to experience hands-on reality, when things are just working different then expected (which is meant to be possitive feebback). Week 4 and 5 take a lot of time (far over expected 30 hours, more like 60-80).

교육 기관: Lindsey L

2020년 8월 30일

The project was a really good way for me to work on my skills. I rated this course 1* because the instruction was abysmal. Too many instances where additional steps needed to be taken to submit a project which were not included in the instructions. Had to rely on comments from students in the forums to learn what I needed to do. I still don't know how to link a Jupyter Notebook to GitHub. Too many times students projects could not be reviewed because the platform did not allow them to submit a shareable link. I could go on, but after sucking way too many hours of my time trying to complete and submit projects because of the lack of complete information in the course, this course doesn't deserve that much of my time.

교육 기관: Olivia V

2021년 1월 12일

A considerable part of the required work (and grading) is on material not taught in this course or the previous ones in the Certificate (web scraping, turning a Jupyter Notebook into a presentation or a report). Instructions are often unclear ("explore..."). Some technical problems are not mentioned, leading a time-consuming researches, even though it appear in the forums they are known to the teaching staff (using nbviewer to make Folium maps visible in Github).

Overall, it is disappointing to spend so much time relying on Google search when one was expecting content taught and delivered by IBM.

교육 기관: Deleted A

2020년 6월 23일

This course's content is out of date. Students have to rely on the posts of other students to work around issues with the course. This is a real shame as the other courses required to receive the certification are well maintained.

교육 기관: mustansir D

2020년 6월 7일

This course is full of bugs (outdated) and lack of explanation for certain matter is seen in Discussion Forums.

교육 기관: Muhammad F S

2020년 5월 9일

It has been a fantastic journey of completing the 9 course specialization over the past two months!

I practically started with no prior exposure to data science but learned a lot of useful Python skills, tips-and-tricks, and knowledge about data science. The course material and instructors were excellent, and the Jupyter Notebooks were very challenging at times - at least for someone who had left programming around 15-years ago.

The specialization, as it says, is of beginner level but would definitely equip the students to move forward on their own in their quest for data science.

I would suggest adding courses on probability and statistics, linear algebra, and calculus in the specialization.