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Bewertung und Feedback des Lernenden für Applied Data Science Capstone von IBM

4.7
Sterne
6,006 Bewertungen
808 Bewertungen

Über den Kurs

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....

Top-Bewertungen

LD

23. Okt. 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

3. März 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.

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626 - 650 von 812 Bewertungen für Applied Data Science Capstone

von Krishnadev A

30. Jan. 2022

best

von silvio a

18. Jan. 2022

nice

von ENUONYE D J

5. Okt. 2021

good

von Arpan C

26. Apr. 2021

GOod

von Kasi V

6. Nov. 2020

good

von Mohammed A W

2. Okt. 2020

Good

von Shalini S

13. Sep. 2020

Good

von VISHNU T B

28. Juni 2020

Good

von Soumyajit D

7. Juni 2020

good

von A S R

20. Mai 2020

good

von Naveen S P

10. Mai 2020

BEST

von ARIJIT K

28. Apr. 2020

good

von Ashneel k

9. März 2020

good

von iyyanar

3. Jan. 2020

Good

von Manea S I

25. Sep. 2019

nice

von Prabhu M

17. Sep. 2019

good

von Gurnam S

4. März 2019

Good

von Josh H

12. Jan. 2020

AAA

von Talha A

30. Sep. 2019

<3

von Luis a l a

31. Juli 2021

f

von 林昀

3. Apr. 2020

9

von Amy P

25. Juli 2019

This is the final course in the IBM Data Science Certificate and it is primarily focused around a project of your choosing. First, you learn how to scrape data and use the Foursquare API, which is quite helpful as these skills are generally transferrable. Then you'll need to come up with an idea that is loosely related to location data in some way. You'll have several weeks to implement your idea and write a report and a blog post/presentation. The final project is a lot of work.

In my opinion, the grading system could be better. You rely on peer reviews to pass the course, but only one peer looks at your work. Multiple sets of eyes would be fairer and hopefully generate more feedback. The discussion forum aspect could also be improved to promote collaboration and not simply requests to "please review my submission".

All in all, a decent guided Capstone course. Be prepared to do a lot of work on your own as there is not a lot of structure or hand-holding. I am very proud to have completed a formal project/report that demonstrates how much I learned over the course of the IBM Data Science Certificate.

von surya m p

10. Apr. 2020

This course is excellent at teaching all the data science and machine learning skills from a practitioner's perspective. I would strongly recommend it to aspiring data science professionals. Other positives include free introduction to the IBM cloud platform.

Room for improvement include:

1. Improvement to reliability/availability of IBM Developer Skills Network (which was done towards the end of my course) or give it a miss (using IBM cloud platform instead) completely.

2. Assignments should be graded by instructors or through standardised testing. The current peer-graded system seems to be hit and miss. It is not ideal especially for such a long course.

von Dominic M L C L

29. Mai 2020

There were quite a number of tools/apis in the course material that were no longer working, meaning they need to be updated and shows the course material has not been touched in quite some time. For absolute beginners this is problematic as they are not unsure where to search for solutions, and asking in the Discussion Forums does not always return an answer. Aside from that, I found the Capstone Project to indeed be challenging for the level of skills we have obtained from the course, but also figured it forces learners to really search and source for solutions similar to how the real world would force you to do so.

von Ruben G

28. Feb. 2021

This course has been a real challenge for me. I've spent many more hours than planned to complete assignments of week1 and 2. I don't know if that is because of the topic I chose or because of the problems I had with Watson.

In the middle of the course, Watson stopped working ("monthly capacity reached"?). After asking for help in the forum, I didn't get any until 10 days later (and, by the way, what I was expecting). I somehow managed to install Juyputerlab as an alternative solution, but to do it properly and being able to publish some data into Github added more complexity to the challenge.