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

4.7
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6,491 Bewertungen

Über den Kurs

This is the final course in the IBM Data Science Professional Certificate as well as the Applied Data Science with Python Specialization. This capstone project course will give you the chance to practice the work that data scientists do in real life when working with datasets. In this course 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 will be tasked with predicting if the first stage of the SpaceX Falcon 9 rocket will land successfully. 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. In this course, there will not be much new learning, instead you’ll focus on hands-on work to demonstrate and apply what you have learnt in previous courses. By successfully completing this Capstone you will have added a project to your data science and machine learning portfolio to showcase to employers....

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

OM

27. Feb. 2023

The IBM Data Science Professional Certificate is a very good course. lam glad to have covered the course with the help of Coursera. I will put to use all concepts learnt in this Data Science series.

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701 - 725 von 892 Bewertungen für Applied Data Science Capstone

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 Mohammad I

4. März 2023

op

von Ikenna M

8. Sep. 2022

ok

von Josh H

12. Jan. 2020

AAA

von Talha A

30. Sep. 2019

<3

von Koushika

16. Aug. 2022

R

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 Andres F M L

21. Dez. 2022

>>Positive things:

-As well as in the other courses, the material is of high quality and the labs to practice are enough to get a good understanding of the core topics.

-In this last course, the knowledge of the other 9 courses was clearly applied. One can call it a very good recap course.

>>Point to improve:

Please make the practice quices more difficult. Instead of asking if the operations were performed, with only yes/no option as answer, the practice quices can be more similar to the graded quices but a bit easier.

Why? In my opinion, the certifications will gain more prestige among Data Scientists because the effort to finish the course is higher.

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.

von Barry P

5. Jan. 2021

I had higher hopes for this....The videos are excellent, the labs are pretty good. The problem with the labs is they will just dump the code in and expect you to know what it all means. I spent a lot of time googling what the code means for when I apply my own analysis. Begs the question of the value.

Lastly, the capstone was more of the same. A lot of digging on my part and not much help from the videos/labs. Also, many of the labs are outdated and you have to search the forums to find out something was deprecated and to use a new function. JUST UPDATE THE LAB!

von Adegbuyi M A

17. Dez. 2021

This Capstone course takes you through all the knowledge areas from the beginning of the course. From Github, to Watson Studio, to IBM Skill Network Labs to outright use of your own resources. I wish all the resources to learn all you have to is concentrated in one location. However, I think the course contents need to be overhauled for improvement by addressing some issues pointed out by learners. Overall, it's been great but be ready to learn hard while using the learners forum.

von Rafael T

21. März 2020

The course is good. It makes you think on all the knowledge acquired during all other 8 courses and make you put in practice.

The only drawback in my opinion is that the course relies on an unreliable platform for Jupyter Notebooks. Several times I wanted to access my notebooks to continue with the course and got a lot "Bad gateway" problems and slow responses in general. It was frustrating because the best part of the course are on the notebooks.

von Jeffrey G

28. Juni 2020

Overall, a very good value. Introduces new topics in Data Science well. Although the Capstone is suitably challenging, I still feel as though there must be a different format to help solidify the coding syntax for python pandas so that the learner doesn't have to rely so much on referencing StackOverflow or previous lessons. Those portions of the code remain a lot of cut and paste rather than truly building the knowledge base.