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Applied Data Science Capstone(으)로 돌아가기

IBM의 Applied Data Science Capstone 학습자 리뷰 및 피드백

4.7
별점
4,722개의 평가
569개의 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

Oct 24, 2019

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

Mar 04, 2020

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 566개 리뷰 중 26~50

교육 기관: Vicky G

Aug 06, 2019

This course did not provide enough learning materials for students to complete the project. For example, it asks students to scrape a web page and parse the table on the website and put it into a pandas data frame in a Jupiter notebook.

교육 기관: Pawel L

Jul 14, 2019

To much focus on Foursquare API

교육 기관: Hakki K

Jul 09, 2020

Hi,

I completed entire program and received the Professional Certificate. On the Coursera link of my certificate "3 weeks of study, 2-3 hours/week average per course" is written. This information is not correct at all, it takes approximately 3 times of that time on average! I informed Coursera about it but no correction was made. It should be corrected with "it takes approximately 19 hours study per course" or "Approx. 10 months to complete Suggested 4 hours/week for the Professional Certificate".

Here is the approximate duration for each course can be found one by one clicking the webpages of the courses in the professional certificate webpage: (*)

Course 1: approximately 9 hours to complete

Course 2: approximately 16 hours to complete

Course 3: approximately 9 hours to complete

Course 4: approximately 22 hours to complete

Course 5: approximately 14 hours to complete

Course 6: approximately 16 hours to complete

Course 7: approximately 16 hours to complete

Course 8: approximately 20 hours to complete

Course 9: approximately 47 hours to complete

This makes in total approximately 169 hours to complete the Professional Certificate. As there are 9 courses, each course takes approximately 19 hours (=169/9) to complete.

(*): https://www.coursera.org/professional-certificates/ibm-data-science?utm_source=gg&utm_medium=sem&campaignid=1876641588&utm_content=10-IBM-Data-Science-US&adgroupid=70740725700&device=c&keyword=ibm%20data%20science%20professional%20certificate%20coursera&matchtype=b&network=g&devicemodel=&adpostion=&creativeid=347453133242&hide_mobile_promo&gclid=Cj0KCQjw0Mb3BRCaARIsAPSNGpWPrZDik6-Ne30To7vg20jGReHOKi4AbvstRfSbFxqA-6ZMrPn1gDAaAiMGEALw_wcB

교육 기관: Pablo V V

Apr 02, 2019

more exercises, more projects.

교육 기관: Ferenc F P

Feb 26, 2019

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

Oct 21, 2019

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

Jul 14, 2019

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

Jul 23, 2019

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

Jan 09, 2019

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

Jan 26, 2020

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

Dec 16, 2018

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

Mar 30, 2019

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

교육 기관: Armen M

Dec 08, 2019

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

교육 기관: Garima K

Mar 31, 2020

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: Muhammad F S

May 09, 2020

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.

교육 기관: Olin H K

May 16, 2020

What a wild ride! The IBM Data Science Professional Certificate was my first exposure to coding and Data Science. The courses prior to the Capstone really do give you enough rope where you can do some cool stuff for the Capstone Project. Throughout the program, I learned where to go when I needed to teach myself something, while I was exposed to the basics.

I'm probably not normal, but I spent 40 or so hours on the capstone project itself. Lots of Frustration at times, but lots of fun as well and I was proud of what I was able to accomplish.

교육 기관: Hayford T

May 13, 2020

When I started this course I didn't know how fast I could grab these concepts in Data Science, it has been a challenging journey, especially learning a new programming language Python with all the libraries and packages, but Don't underestimate the little efforts, It leads to greatness. Now through Coursera and IBM I can boast courses that has prepared and given me head start into my new career. Coursera has made it possible for me to study at my own pace. This is amazing!, it worth recommending!.

교육 기관: Atfy I Z

Apr 27, 2020

A great course that tests your skills to apply your cumulative knowledge on Data Science since you embarked on the Programme.

As for me, even though I don't intend to become a full-fledged Data Science, this course along with the Specialisation Programme provide sufficient understanding and practical hands-on learning to better appreciate benefits and constraints of Data Science, particularly on the importance of data and machine learning ability.

교육 기관: Dayli S

Apr 30, 2020

I really like using everything I learned into my own example built from the beginning to end. I felt a little bit unsecured with the assignment at the beginning, because I was really a beginner in Python before I started this course. But it encouraged me a lot to finish it and to learn more. Now I have a basis and a lot of information, that I need to sort out. I am planning to do more Python courses and continue practicing.

교육 기관: Marvin L

Oct 31, 2019

It was very good.. Overall. few things I like to add.

Sharing my notebook from Cloud was not working a lots time.

GitHub , or Jupiter Notebook with simple 2 lines of coding did not work..

Also, a lot of time, cloud machine just spins.. -- without showing it

My resource got close to limit , - Could not add more code..

Instructions were out of date, could not be applied current version

Working with Cloud machine was challenging !!

교육 기관: Julien P

Feb 23, 2020

Great to put into action the theoretical knowledge acquired in the previous 8 classes. What could be improved? The peer-rating system can be very slow. A common practice is to go on the forum to beg for a rating by another learner. This can be tiring.

교육 기관: Yibing S

Jul 10, 2019

This course is instructive and challenging at the same time. Now I do wish I know a bit more about python and pandas before I jump in this course. But in the end I managed to get through.

교육 기관: Ian C

Apr 26, 2019

Felt a bit constrained by the requirement to include the Foursquare API.

교육 기관: Nikolay D

Dec 27, 2018

Very easy to understand and remember this material

교육 기관: Ozgur U

Jul 03, 2020

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.