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Applied Data Science Capstone(으)로 돌아가기

IBM 기술 네트워크의 Applied Data Science Capstone 학습자 리뷰 및 피드백

6,133개의 평가

강좌 소개

This capstone project course will give you a taste of what data scientists go through in real life when working with real datasets. You will assume the role of a Data Scientist working for a startup intending to compete with SpaceX, and in the process follow the Data Science methodology involving data collection, data wrangling, exploratory data analysis, data visualization, model development, model evaluation, and reporting your results to stakeholders. You are tasked with predicting if the first stage of the SpaceX Falcon 9 rocket will land successfully. SpaceX advertises Falcon 9 rocket launches on its website, with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because SpaceX can reuse the first stage. Therefore if you can accurately predict the likelihood of the first stage rocket landing successfully, you can determine the cost of a launch. With the help of your Data Science findings and models, the competing startup you have been hired by can make more informed bids against SpaceX for a rocket launch. This course is the final course in the IBM Data Science Professional Certificate as well as the Applied Data Science with Python Specialization. It is expected that you have completed all of the prior courses in the specialization/certificate before starting this one, as it requires the application of the knowledge and skills taught in those courses. In this course, there will not be too much new learning, and instead, the focus will be on hands-on work to demonstrate what you have learned in the previous courses. 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....

최상위 리뷰


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


2020년 3월 3일

Very good capstone project. Learnt lot of insights on how to represent data through out this course.

Very good starting point for ""Data Science" field. I would definitely recommend this course.

필터링 기준:

Applied Data Science Capstone의 829개 리뷰 중 751~775

교육 기관: alberto i

2019년 8월 1일

Kind of difficult for beginners

교육 기관: 王童燕

2020년 1월 7일

Interesting projects included!

교육 기관: Napattarapon P

2019년 9월 11일

Useful course for starter

교육 기관: Hardik R S

2019년 2월 24일

Little bit hard

교육 기관: Robert B A

2022년 4월 20일

A great course

교육 기관: adetunji p

2022년 2월 23일

it was awsomee

교육 기관: Deepak N

2019년 8월 12일

Good exposure.

교육 기관: YIFAN H

2019년 11월 10일


교육 기관: Angam P

2019년 9월 15일

great course

교육 기관: Ernesto C M P

2022년 1월 16일

good course

교육 기관: zoubair a

2020년 6월 2일

good course

교육 기관: Magnus B

2019년 6월 10일

Fun course!

교육 기관: Abdulla M

2020년 11월 6일

very good

교육 기관: Amanullah K

2020년 10월 31일


교육 기관: Satishkumar M

2020년 1월 9일


교육 기관: Prayag P

2020년 7월 30일

Good !

교육 기관: Narmeen i

2021년 9월 10일


교육 기관: Andrian R N

2021년 8월 15일


교육 기관: Song N W

2022년 4월 3일

My review after around 11 or 12 months of studying all ten courses is: the IBM Data Science Professional Certificate contains general information and process on data science, but does not go into great detail. After joining the course, I was able to further think and ask at work how data collection would affect our product and indicate how the envisioned data may provide misleading information. However, the courses' duration is much longer than indicated on Coursera and apart from the time spent on completing the course contents, I have spent a lot of time trying to get Watson Studio or the hands-on lab operational, and the hands-on lab could be down for a week or more. From my perspective, this is counterintuitive and inefficient, and I did not expect this from IBM's products. Looking at some of the reviews for this course, I think it is fair to warn others who are looking to join that the courses are generic and some - if not most of the - time requires the learner to Google answers for any confusion and code tutorial or read books; this does not bother me as it has been my approach for a long time. As I am writing this review I am awaiting my classmate to review my peer assignment for the second time because I got less than the required pass grade, which perhaps my classmates may have experienced once or twice throughout these ten courses due to incorrectly marked assignments. Although I admit I did not perfectly complete the capstone project's slides, based on the guide I would have received a higher score than what I had; for example, I got zero points instead of two points for having my GitHub link and PDF file attached. However, I understand the importance of reading your peer's assignments because some of the assignments I read inspired me, but the peer-review system is flawed because the course expects a constant number of students studying at the same period and the minimum number of assignments required the students to review would cover the whole cohort for that period; evidently, sometimes this system does not operate as expected because there are usually threads asking for help to review the assignment on the week's discussion forum. Consequently, from my perspective, this course would be more efficient if an automated correction system was also available, similar to "Machine Learning by Andrew Ng".

교육 기관: Tania D

2020년 8월 21일

The assignments were interesting especially when we had to think of our own problems to solve. It would've been really helpful if the course was regularly updated, specifically when it comes to the first assignment where a lot of students experienced challenges with their machines and the course was designed with old operating machines in mind. the discussion forums would help a lot if instructors actually answered the questions and not directed students to links that were of no assistance at all. The course material could really do with an upgrade.

교육 기관: Marius S

2020년 10월 14일

The github guide was very helpful and informative. I wished there would have been more explanations how to interprete the results of evaluated models and about machine models in general, when to use which model for example. Also some details were missing like how to balance imbalanced data, should the data be balanced and then visualized or vice versa? Fitting a model is easily done, but it's the details that make the difference.

교육 기관: Deleted A

2020년 7월 31일

The course gives the learners a perfect platform to practice the concepts learnt throughout the Data Science specialization. Final assignment is unique and interesting and the course makes sure you practice enough before taking it up. A good experience, but issues with Foursquare now and then makes it a little hectic to get done with the course.

A nice course though. Liked it!

교육 기관: PRANJIT G

2020년 6월 2일

The journey was very informative but at the end of the course and submitting all of my assignments before the deadline , i got my course certificate with no instructor signature . A very disappointing fact as I worked very hard to complete the specialization within my 7 day free trial which is till 8 pm today I.S.T

Never expected such kind of irresponsibility

교육 기관: Shane W

2020년 11월 18일

The capstone content needs to be thoroughly edited for clarity, especially in the instructions on what exactly the instructors are looking for in the final deliverables. Some of the peer-reviewers seem to be confused, and I'll admit I was a little confused myself reading through the instructions the first time.

교육 기관: Bart F

2020년 10월 9일

The final capstone felt like a mis-match with the previous 3 courses of the specialization. The capstone was also the final project for other specialization which would explain the mis-match.

Even though I learned a lot, it wasn't entirely clear what was actually required from me.