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.
TM
Very challenging but truly rewarding. I learnt a lot through the mistakes I made throughout the project but there is a lot of support on the forum and the internet to help with any issue.
SR
It has been one heck of a journey, there were moments of confusion but then followed by the joy of eureka. I have found the course very useful. Special thanks to the creators and the instructors
YS
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.
OM
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.
CS
It's a great course to get a comprehensive background on Data Science (including ML) and lays the foundation for more advanced courses. It touches on all the areas that are required for data science.
IK
Good course, steps you through all the relevant steps and reasoning. The final project is graded more on your ability to put together a power point than your understanding of the material though.
MK
Excellent chance to push in every concept learnt so far. Best thing about the course is the chance to self identify a problem and find the solutions all by ourselves. Great Content. Thanks !
TG
Very difficult to manage the scope, but it is a self-learning process. Recommend extending the Capstone course another week or two, to encourage the students to go all in on their work.
JA
Lots of outdated content and not many resources to help, however a lot of it was at least a little bit useful. Though a lot could be cut if you already know a thing or 2 about programming
SG
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.
SJ
Last week took me too much time, but that was my own laziness. The course is well designed and taught me a lot about data cleaning, and strict deadlines are also a good learning. Thank you.
AG
Thank you for this course. I get know experience and knowledge in using different kinds of online tools which are useful and effective. I'll use some of them during my lessons. And lots of thanks. JR
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Instead of only peers review, I think it would be better if someone professional also review our capstone project and gives us feedback
Capstone implies apply what you learned throughout the program. I appreciate that you were able to work in things you learned of your choice if you wanted to, but the sole requirement and focus of the capstone shouldn't be to include something that was just introduced. Also, the graded lab that isn't part of the final project cannot be completed with the knowledge provided by this course. I should NEVER receive instructions to check out more help on YouTube from a course for which I am paying. I will not recommend this course to anyone.
Stay away. Instead of taking the time to teach the material requested in assignments it tells you to go to youtube and teach yourself. It's a pathetic finish to a certification course with such a prestigious company name attached to it.
Had to provide a credit card for FourSquare which I did not appreciate
I was not too happy with the way peer grade assignment was done and it is being used as a mechanism to grade the course. My submissions were not reviewed correctly and because of that I felt that the course duration prolonged for one more month, and I had to pay my subscription for one more month for no reason.
This a capstone project as part of my IBM Data Science professional certificate. The course was intended in my view as a recollection of the material and practices taught in the course all rolled into a nano project.
In my view the topic and objectives for this course were very loose but the main focus assumes to be foursquare API's which were not mentioned until this course. The topics were loose which meant issues encountered in data collection via scraping or visualization might not have been covered within the course.
For me it was a learning experience but I am not sure about the value of the certificate offered by IBM. For starters the grading is peer reviewed which of course means the grade are not quality controlled but a reflection of your peers grading abilities regardless of whether they would like to shoulder those responsibilities.
There is no proctor administered for graded examination or office spaces for internship like experience and assumes some higher power is reviewing your honor code and motivation.
As far my application with this certificate has not given me a technical advantage even within IBM and I am not sure if the third party career counseling offered via Coursera have a success story.
I enjoyed the course even though the course and its material very rough and marketing oriented. The course was supposed to provide me with a practical advantage in the area of Data Science as a new entrant and I have not observed any advantages so far with my applications on linked in.
Quizzes are poorly designed. Evaluates only memorized information.
Utterly useless. It mandates a learner to work on geo data related project only.
And that too using a very specific api and data.
No option to work or submit meaningful capstone projects.
Utilizes skills learned throughout previous courses and puts it all together in Capstone assignment. Found the instructions to be lacking but it is a Capstone so not totally unexpected. I did find some of the instructors comments in the forum to be somewhat unprofessional so maybe some coaching should be done on how to respond to students who pay to take the course; even those that frustrate you!
I liked working on a project from beginning to end - finding a problem to solve, acquiring data, creating & testing hypotheses. It really puts what you've learned to test. I also learned some techniques that were not covered in the course and other skills like creating Medium posts.
I didn't like the review aspect. You only have to review 1 project and receive a review from 1 person which is not enough. I reviewed multiple submissions and found it very educational. The review criteria are vague and mostly cover just the presentation of results. They don't assess the quality of analysis itself (and it's difficult for an unexperienced person to do without guidelines). Getting feedback from more people would be interesting.
The discussion forums are not helpful since they're spammed with "Please review" threads - and the staff doesn't do anything about it. This applies to the whole specialization.
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
The only problem is that I ran out of hours using IBM watson and the same thing happened with Foursquare when I reached the maximum numerber of records per day and per hour.
We as students should have tools where we can make mistakes without reaching 'limits of usage'.
The worst course by far in the 9 courses bundle
Please consider remove this course from the bundle
There should be a clear tutorial on how to scrap a website. The project should be more open and not tight to using Foursquare, and should not be reviewed by other students.
The blind leading the blind.
Vague and confusing instructions. You are trying to teach new data scientists to do a business case analysis, but you just threw some random data and some generic methodology instructions at them. How can I demonstrate the process of coming to a 'business understanding' ? There's no client, and no goal!
I am genuinely unsure whether you consider it important to use the Seattle accident data, or whether I can use some other kind of data and solve an unrelated problem. The "good" example provided for the last week is something completely unrelated. If I choose something unrelated, too, do I have to risk a bad grade from my peers who don't understand it?
To add insult to injury, there doesn't seem to be any instructor available to answer the many, many desperate questions from learners on the forum.
The whole "peer review" thing needs to be re-through, especially when dealing with such a vague assignment. And the system of everybody spamming the forums with review requests, so you can't find any actual topics for discussion with other students, is a hot mess. And, let's face it, "peer review" is pretty useless anyway (and potentially discriminatory, by the way). If I had known the grading would be on this basis, I would never have bothered with this course.
This was challenging and a project is always the only way to really learn anything and struggle through. I did however feel that the forum for this course was not useful and that the mentors/lecturer's let us down. They hardly reply to relevant questions leaving students to feel abandoned. With a project you need some support as many students are not 100% comfortable with the code. One of the other courses has the forum broken up into two separate forums: One for the tech questions and another for requests from students to review - this was clever and worked well. Im giving this 2 stars based on the support and lack of direction for the project. May have also been nice to get options for a dataset - not easy to find a dataset in the public domain (spend hours looking for good ones)
Mentors won't teach how to use foursquare API and how to apply different ML algorithms. As the certificate names professional Data Science there is no introduction or explanation about neural networks. A deployment part is needed.
The project was a really good way for me to work on my skills. I rated this course 1* because the instruction was abysmal. Too many instances where additional steps needed to be taken to submit a project which were not included in the instructions. Had to rely on comments from students in the forums to learn what I needed to do. I still don't know how to link a Jupyter Notebook to GitHub. Too many times students projects could not be reviewed because the platform did not allow them to submit a shareable link. I could go on, but after sucking way too many hours of my time trying to complete and submit projects because of the lack of complete information in the course, this course doesn't deserve that much of my time.
The assignment is in and of itself nice, but it is too free. I would have liked a more restricted assignment. During the specialization we mostly had to watch how stuff was done, without much practice on our own, so the step to this assignment was quite large, especially if you are graded by your peers. We were also graded on stuff that was not part of the course, such as reports and presentations. I understand that this is important, but not the aim of the course. Furthermore, we were graded by peers, who are the same level. How can they grade a submission if they themselves do not yet know what is good and what is not?
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.