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

4.7
Sterne
6,005 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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726 - 750 von 812 Bewertungen für Applied Data Science Capstone

von Siwarak L

11. Feb. 2020

The detail of the course is quite limited.

von Abdul S M G

19. Apr. 2020

Good introductory course on Data Science.

von Raajasree R

5. Feb. 2022

I completely enjoyed doing this course

von rk s

20. März 2022

Great course for absolute begineers.

von Umesh S

9. Aug. 2020

please update the practice notebooks

von Yu M C

24. Dez. 2019

very long to get anything reviewed.

von alberto i

1. Aug. 2019

Kind of difficult for beginners

von 王童燕

7. Jan. 2020

Interesting projects included!

von Napattarapon P

11. Sep. 2019

Useful course for starter

von Hardik R S

24. Feb. 2019

Little bit hard

von Robert B

20. Apr. 2022

A great course

von adetunji p

23. Feb. 2022

it was awsomee

von Deepak N

12. Aug. 2019

Good exposure.

von YIFAN H

10. Nov. 2019

真的难,对我这个初学者来说

von Angam P

15. Sep. 2019

great course

von Ernesto C M P

16. Jan. 2022

good course

von zoubair a

2. Juni 2020

good course

von Magnus B

10. Juni 2019

Fun course!

von Abdulla M

6. Nov. 2020

very good

von Amanullah K

31. Okt. 2020

Excellent

von Satishkumar M

9. Jan. 2020

Average

von Prayag P

30. Juli 2020

Good !

von Narmeen i

10. Sep. 2021

good

von Andrian R N

15. Aug. 2021

Cool

von Song N W

3. Apr. 2022

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".