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Voltar para Data Science Methodology

Comentários e feedback de alunos de Data Science Methodology da instituição IBM

4.6
estrelas
16,628 classificações
2,016 avaliações

Sobre o curso

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....

Melhores avaliações

AG
13 de Mai de 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 de Jun de 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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1701 — 1725 de 2,016 Avaliações para o Data Science Methodology

por Prabhandini V

5 de Jan de 2020

Nice course

por Stephane B

14 de Abr de 2019

Nice course

por Jairo S B

26 de Jan de 2021

Very clear

por Abdulwasiu B

23 de Jul de 2020

Very good,

por JORGE D

26 de Abr de 2020

excelent!

por David L

21 de Mar de 2020

Thanks!!!

por Abdelhamid G

24 de Dez de 2019

very good

por Koyya S

4 de Mar de 2019

Thnakyou.

por ShanQiu

11 de Mai de 2019

Not bad~

por Amarzaya

4 de Mar de 2020

liked

por Osuolale E

19 de Jan de 2020

Great

por Raja M N

7 de Jul de 2021

good

por Muhammad S A

14 de Out de 2020

good

por Shone G

22 de Jul de 2020

good

por Pagadala G s

2 de Mai de 2020

Good

por G V

10 de Mar de 2020

good

por Satishkumar M

4 de Jan de 2020

G

o

o

d

por Akhil K

14 de Mai de 2019

Good

por Savita M

11 de Jun de 2019

4.5

por Shruti R

28 de Abr de 2020

NA

por Adil S

27 de Jan de 2019

AS

por Néstor R V M

12 de Nov de 2018

:)

por Daniel L A

22 de Jun de 2019

-

por Andrei P

13 de Abr de 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.

por Francisco M

8 de Fev de 2021

The course "data science methodology" provides a reasonable good overview of the main stages of a data science project based on a methodology similar to CRISP-DM methodology. Explanations are supported by two main examples: one related to "reducing risk readmission of patients in a hospital" and the other related to a study of "food recipes". In addition, some Jupyter Notebooks have been developed to make the course more practical. In general, I believe three weeks is not sufficient to cover all the phases of a data science project with enough detail. I think the video recordings do not provide clear and sufficient explanations of the different phases of a data science project. I also believe the course should provide further details, examples and Jupyter Notebooks to better address relevant issues in each of the phases of a project. I would add more "optional content" for students interested of additional details and examples. The final assignment seems not to be sufficient to prove that a student has understood the material. I think this course might benefit if students are already familiar with programming languages such as Python and query languages such as SQL (in other words, please consider adding these prerequisites for the course).