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Avis et commentaires pour d'étudiants pour Data Science Methodology par IBM

4.6
étoiles
17,145 évaluations
2,094 avis

À propos du cours

Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand. This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand. Accordingly, in this course, you will learn: - The major steps involved in tackling a data science problem. - The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment. - How data scientists think!...

Meilleurs avis

AG
13 mai 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 :)

TM
18 juin 2021

Very interesting course. It shed a light on what the structured approach really is. It's worth to pause for a moment with every step of the methodology and think how to apply it in real life. Thanks!

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1851 - 1875 sur 2,094 Avis pour Data Science Methodology

par Gayatri H

31 mai 2020

The final assignment is not very specific. Its's largely open ended and left up to individual discretion. Please make it quiz based or project based, where results are definitive.

par Vara P

22 juin 2020

The core part of the methodology is not properly covered, it would have been better if the technical information such as tools and the modeling strategies are discussed in depth.

par Farzana S

14 mai 2020

The course was OK but not up-to the Mark,the case study was quite tough to understand,the case study could have been something simpler so that the beginners can understand well.

par Ryan K

8 nov. 2018

Using a case study to illustrate the ideas is great. But it will be better if a less complicated example can be used to help following the concepts easier and better understood.

par Chetan K

16 janv. 2020

An easier and more relatable case study would exponentially increase understanding. The present one had complex medical terms and added a layer of complexity to the course.

par adwayt n

5 mars 2019

It was very theoretical and, at times, a little boring as well. I was hoping for more of a hands on experience. But it was definitely very instructive and educational.

par Ramkumar G

24 sept. 2019

Since we are following a track, the previous two courses were basic and in this course we came across to lot of data science terminology without proper introduction.

par Umaimah Z

8 juil. 2019

The example provided was not good at all to follow on the concepts. i had a hard time following up with the video since very little time was spent on each concept.

par Marnilo C

25 avr. 2019

The discussions were too introductory. This would be acceptable had there been links to resources which provided more detailed information on this important topic.

par Micatty B

9 déc. 2019

The final assignment is not clear

Data preparation and modeling quite confusing for someone with no prior knowledge in data manipulation and statistical background

par Jayan T

22 oct. 2018

Its an important topic for data scientists, but wish it was taught in a more interesting way with multiple examples of different types instead of one case study.

par Siddhartha P

21 avr. 2019

Very short and filled with too much jargons. A much simpler case study would have been great instead of deep diving into the world of Life Science & Healthcare

par Declan H M G

13 mai 2019

I found the material here vague and difficult to follow at times. Which led to confusion particularly about what was expected with the peer graded assignment.

par Головатенко В В

25 janv. 2020

Well, when the previous courses in the specialization were a total waste of time, this one is adequate, but still not very usefull for data science itself.

par Wilbert V G

3 juin 2021

The speaking is too fast and the slides don't help much to follow up the explanation. I found it is better just to read the transcript at my own pace :-)

par Alok M

12 janv. 2020

Better problems (more generalized and relatable) could have been used to describe and make the modules understand better. Not satisfied with this course.

par olu

31 mars 2020

Teaches what it's supposed to but could be more indepth in establishing your understanding of the process of methodology from Analysis to Evaluation

par Alirz110

4 mai 2021

This information is replicated and immersed in the workplace of a data science expert.

In my opinion, there was no need to attend a separate course.

par Lahiri B

13 oct. 2020

Questions asked during the course videos were repetitive and three of them could not be submitted due to some error, despite trying multiple times.

par SAMYAK S

14 avr. 2020

It's good but I think the case study is not easy to understand and another case study must be included which easily makes you understand this topic

par Mark H

30 janv. 2019

Course was ok. It's difficult to formalize data science into a generic methodology where subject matter expertise is separated from the process.

par Baptiste M

25 oct. 2019

IBM Developer Skills Network tool is a complete disaster, spending more time trying to get what should have been a PDF than actually studying...

par Pedro C F

18 févr. 2020

the case study is not easy for someone whos is doing it for first time, also you need more text for explaining the approach analytic.

D.S Pedro

par Jennifer K

4 avr. 2019

The topic is super important and interesting. The content of the course was a bit hard to follow. More real-life examples may have helped.

par Sai T

22 oct. 2019

The examples used were poor and the definitions of each stage were not concrete, workable definitions but rather very abstract definitions.