Learner Reviews & Feedback for Data Science Methodology by IBM
About the Course
Top reviews
AG
May 13, 2019
This is a proper course which will make you to understand each and every stage of Data science methodology. Lectures are well enough to make you think as a data scientist. Thank you fr this course :)
JM
Feb 26, 2020
Very informative step-by-step guide of how to create a data science project. Course presents concepts in an engaging way and the quizzes and assignments helped in understanding the overall material.
2401 - 2425 of 2,575 Reviews for Data Science Methodology
By David N B
•May 24, 2019
Very little assistance from Moderators
By kamal b
•Dec 22, 2022
The tutor needs to be more involving.
By Tushar M
•Jan 24, 2020
the course content requires an update
By Deepratna A
•Jun 22, 2019
Could have made it more interesting
By Thomas M
•Oct 17, 2019
Not great quality of video content
By ABHIJEET B
•Jun 3, 2019
CHF case study was the worst part
By Ali R ( R
•Oct 3, 2018
The case study was hard to follow
By Ramakrishna B
•Jun 10, 2019
More explanation would be great.
By Glenda m
•Mar 25, 2020
Falta mas ejemplos descriptivos
By sairam p
•Apr 11, 2019
concentrated largely on theory.
By Anup U
•Jul 19, 2020
it should be more descriptive
By usha y
•Sep 18, 2018
very nice course knowledgeble
By Lawrence L
•Jul 11, 2023
Too many narrative exercises
By Arushi
•Apr 11, 2022
Its very very theoritical.
By SANDHI J
•Jun 28, 2021
Should be more interactive
By Salvatore P
•Oct 17, 2021
too much simplicistic.
By Igor L
•Oct 2, 2019
Too basic and too easy
By Ar R H
•May 16, 2020
The journey was well
By José M P A
•Jan 3, 2019
A little boring...
By Richard B
•Feb 3, 2021
good start.
By George Z
•Jun 16, 2019
Very boring
By Rohit G
•Apr 30, 2018
Nice course
By Max W
•Nov 10, 2018
bit boring
By Roxana C
•Jan 10, 2022
This course was fairly disappointing. Apart from the actual steps of the methodology, it does not properly teach the concepts mentioned in the course. For instance, the ROC curve used in the case study: I actually understood how it works from the forum, because one of the admins was kind and has given a very professional and well explained answer. I wouldn't say this course is a waste of time, but I believe it addresses superficially most concepts. I am a firm believer in explaining only a couple of things and doing them very well. The labs are bridging some gaps, so extra points for that. The chosen case study is not thoroughly explained - it uses methods that we are not given any context for and only the very obvious elements are explained. The parts addressing the case study need a serious revision. If you are not following the Data Science Specialization, I would recommend you find a better course on Data Science Methodology - this course is not it.
On the plus side, I did like the final assignment: yes, it is theoretical, yet it helps you really revise all that you've learned in the course.
By Oliver K
•Oct 7, 2024
Several errors throughout the course. 1. Module 2 - default Jupyter cell output does not show Cuisine column mislabeling and inconsistency as is stated in the text. 2. Module 3 - This question is barely english. "For predictive models, a test data set, which is similar to but independent of the training set, is used to determine how well the model predicts outcomes—using a training or test. A test data set happens during which stage in Foundational Data Science Methodology?" 3. Module 3 - "Which of the following statements describes how data scientists refine the model after the initial deployment and feedback stages?" Apparently the correct answer is "By incorporating information about participation and possibly refining with detailed pharmaceutical data." Note that the question does not refer to the case study but asks generally about Data Science Methodology, but the answer talks about pharmaceutical data...