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Learner Reviews & Feedback for Data Science Methodology by IBM

4.6
stars
19,899 ratings

About the Course

If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data Scientist is it. In this course, you will learn and then apply this methodology that you can use to tackle any Data Science scenario. You’ll explore two notable data science methodologies, Foundational Data Science Methodology, and the six-stage CRISP-DM data science methodology, and learn how to apply these data science methodologies. Most established data scientists follow these or similar methodologies for solving data science problems. Begin by learning about forming the business/research problem Learn how data scientists obtain, prepare, and analyze data. Discover how applying data science methodology practices helps ensure that the data used for problem-solving is relevant and properly manipulated to address the question. Next, learn about building the data model, deploying that model, data storytelling, and obtaining feedback You’ll think like a data scientist and develop your data science methodology skills using a real-world inspired scenario through progressive labs hosted within Jupyter Notebooks and using Python....

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.

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1776 - 1800 of 2,502 Reviews for Data Science Methodology

By Nora S I

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Dec 18, 2018

Quite complete. I recommend it. It didn't get 5 stars, because too many concepts were just brushed over, but it is an excellent review of how to handle data

By Aiman A A G

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Mar 1, 2022

It would be even better if we are provided with the slides to review by ourselves before the final exam instead of needing to go through the videos again.

By Deleted A

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Aug 30, 2021

Very well designed! Some of the best courses! Good videos with illustrative images and quality sound. Would be nice to get some help with the lab exercises

By Joana M D V P M S

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Jun 3, 2020

Great content and clearly explained. I would just add more practical exercises to consolidate all the information provided. But I really enjoyed. Thank you

By Sai P B

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Jul 15, 2019

This gives a basic knowledge of Data Science and explains neatly in every steps. Appreciate your efforts in putting this course in neat and expressive way.

By Rajesh W

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Sep 10, 2018

Some concepts are difficult to understand, probably because I am hearing them for the first time. Hopefully, next section of the courses will address this.

By Vijayalakshmi K

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Jun 26, 2019

Great course with lot of good information.. but a bit over the head stuff for non technical people due to all the Python language included in the course.

By Anuar M

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Feb 26, 2020

Good overview of the data science methodology. However, to fully understand the topic, need to do more practices and hand-on on the real world project.

By Michael P

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Nov 16, 2018

Teaches you how to think like a data scientist within the business context. Instructor could do a better job at explaining things the quiz tests you on.

By yessir

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Jul 20, 2023

great course but hard to understand using heart failure situation as example, that i think most of the people hard to imagine it because its not common

By Koji J

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Dec 28, 2020

Very structured content which is easy to understand. A case sudty with less medical terminology would be easier to understand for non-native speakers.

By SARVESH P

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Mar 23, 2020

The case study used in the course was too complex to understand, choosing different case study to explain the concept is more beneficial for students.

By Scott G

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Sep 17, 2022

Generally very good content. I would like to have learned more about the various analytic approaches with more examples of when each is appropriate.

By Roman I

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Sep 6, 2022

Good overview. However, a simpler example rather than medical could be used. The medical terms are difficult to understand for a non-native speaker

By Soumyajit C

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Nov 14, 2018

There is bug in the submission page of peer graded assignment. I had to submit thrice. Only a part of my answer was being uploaded after submission.

By Chris G

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Feb 1, 2023

Good information but the first few sections were frustrating because you can't really answer questions about a complex model till you use it a bit

By Denis R

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Dec 12, 2019

More time could have been spent on model evaluation as it is the most complex topic. Otherwise the class is very interresting and well structured.

By CINDY

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Nov 4, 2018

An introduction class for those who never get in touch with Data Science, but for people who learnt this before, it is definitely a waste of time.

By asher b

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Oct 11, 2018

Good overview of a "scientific method" applied to the field. this might be a better choice for the introductory course in the certificate program.

By Lane G

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Nov 29, 2022

Overall a good beginning course for data science methodology. Some of the steps and processes could have been explained definitions/explanations.

By Md A I

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May 22, 2019

Grading procedure is very weak and course has synchronization of lack of lab and theory. The lab seems very difficult with lots of python coding.

By Frederico C V

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Oct 10, 2019

It could be much more interesting if we had the image of someone explaining, if we could see someone, that could show excitement on the subject.

By Sucheta

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Aug 12, 2019

All steps of data science methodology are explained very well. Final assignment could have been more challenging (with some more quiz questions)

By Student p

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Dec 19, 2022

This course is really very helpful for upcoming data scientists. i enjoyed a lot this course and i hope this will give me benefit in future.

By Niko Y

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Jul 21, 2023

simple but not easy. But I would like it if they could include a written assignment in between the course too before the final assignment.