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Data Science Methodology に戻る

IBM による Data Science Methodology の受講者のレビューおよびフィードバック

4.6
13,119件の評価
1,460件のレビュー

コースについて

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! LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate....

人気のレビュー

AG

May 14, 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 27, 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.

フィルター:

Data Science Methodology: 1176 - 1200 / 1,455 レビュー

by Obong G

Jan 30, 2019

great course

by ADU I O

Feb 14, 2020

Impressive!

by Prabhandini V

Jan 06, 2020

Nice course

by Stephane B

Apr 15, 2019

Nice course

by JORGE D

Apr 26, 2020

excelent!

by David L

Mar 21, 2020

Thanks!!!

by AbdElhamid I A A

Dec 24, 2019

very good

by Koyya S

Mar 04, 2019

Thnakyou.

by ShanQiu

May 12, 2019

Not bad~

by Amarzaya

Mar 04, 2020

liked

by Pagadala G s

May 02, 2020

Good

by G V

Mar 10, 2020

good

by Satishkumar M

Jan 04, 2020

G

o

o

d

by Akhil K

May 14, 2019

Good

by Savita M

Jun 11, 2019

4.5

by Shruti R

Apr 28, 2020

NA

by Adil S

Jan 28, 2019

AS

by Néstor R V M

Nov 12, 2018

:)

by Daniel L A

Jun 22, 2019

-

by Andrei P

Apr 13, 2019

The information was somewhat confusing at times and it was kinda hard to follow the lectures even though the information provided was quite basic nad not too complex. I guess the problem with this course is the way the information presented and the overall flow of the presentation.

Also the labs, they confused me even more because we get presented with some amount of code which was not covered before. You are supposed to be able to complete this course without any coding, but you get all this unnecessary code, which doesn't even matter in the end but adds to the confusion and makes the lab harder to follow. I think it would be better to get rid of the code, or to include these labs after the python course, so the students can easily follow what's actually going on in the labs.

As i figured from the discussion section there is a number of students that were a bit confused about what actually should be in the final assignment (myself included). I had to rewatch all of the videos and revisit all of the labs just to get vague understanding of what needs to be done.

I am still unsure if what i wrote in the final assignment was even 100% correct (even though i got the top score), simply because these assignments are being judged by peers, not mentors.

by Anna N

Apr 17, 2020

This class was OK. Solid introduction to the start to finish process of describing and solving a data science problem. Not super engaging, but that's OK.

My biggest problem is that the step where you turn a business problem into a data science problem is glossed over. I think they called it "analytic approach". It's easily the most important part of the course, and it is given very little attention.

This comes into sharp focus when you try to do the final project, and realize that unless you've done this professionally before, you really don't understand how to ask a question in a way that sets up the data science methods.

As an overview of a method, it's not bad. It really does highlight the iterative nature. But the final project is maddeningly vague and nearly impossible to do due to the "skipped" step inbetween.

I know that they didn't want to teach statistics, or assume people already knew statistics. But then the finally project should have held our hands a little bit better for this one step.

by Rick G

Jul 31, 2019

I wanted more out of this class and I think this entire certificate should use this methodology as the manner in which all the classes and projects are done. It was still good to take to get a good foothold of the methodology, but by structuring that same methodology towards this certificate would go a long way in enhancing the overall experience. The first class could go over the analytic approach. The next three or four over data requirements and gathering data. Another three over the exploration and then use the final two classes or so to go over modeling and tweaking. There's potential for such a concept. Make it so!

by Venkatesh S

Sep 18, 2019

I felt like there was too much emphasis on a top-down approach. Many a time one doesn't have the good fortune of going through the entire data science methodology as mentioned here. The client has already collected the data and then comes and gives you a problem. In this case, you need to have a bottom-up approach - play with the data already collected and see which analytic approach is feasible. In addition, not enough was done to say that this 'story' is the ideal scenario! Rarely do you get the chance to do a data science project so neatly. But it is always useful to know how things would work in a perfect world.

by Marie D

Feb 24, 2020

The actual methodology and the questions to keep in mind for each step are very good, and it's good to have this foundation for understanding data science. But the course was poorly designed and not engaging. Too much jargon was used for a beginner course without explaining what terms mean. There was a glossary in the intro but it was just a list of words with no definitions (were we supposed to look them up??). I'm a native English speaker who works in healthcare and even I felt that the medical case study was too dense to really understand as a case study. The recipe analysis in the labs was much better.