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

18,673 évaluations

À propos du cours

If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data Scientist is it. Most of the established data scientists follow a similar methodology for solving Data Science problems. In this course you will learn and then apply this methodology that can be used to tackle any Data Science scenario. The purpose of this course 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 practicing data science - Forming a business/research problem, collecting, preparing & analyzing data, building a model, deploying a model and understanding the importance of feedback - Apply the 6 stages of the CRISP-DM methodology, the most popular methodology for Data Science and Data Mining problems - How data scientists think! To apply the methodology, you will work on a real-world inspired scenario and work with Jupyter Notebooks using Python to develop hands-on experience....

Meilleurs avis


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!


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 :)

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2176 - 2200 sur 2,325 Avis pour Data Science Methodology


3 juin 2019

CHF case study was the worst part

par Ali R ( R

3 oct. 2018

The case study was hard to follow

par Ramakrishna B

10 juin 2019

More explanation would be great.

par Glenda m

25 mars 2020

Falta mas ejemplos descriptivos

par sairam p

11 avr. 2019

concentrated largely on theory.

par Anup U

19 juil. 2020

it should be more descriptive

par usha y

18 sept. 2018

very nice course knowledgeble

par Arushi

11 avr. 2022

Its very very theoritical.


28 juin 2021

Should be more interactive

par Salvatore P

17 oct. 2021

too much simplicistic.

par Igor L

2 oct. 2019

Too basic and too easy

par Ar R H

16 mai 2020

The journey was well

par José M P A

3 janv. 2019

A little boring...

par Richard B

3 févr. 2021

good start.

par George Z

16 juin 2019

Very boring

par Rohit G

30 avr. 2018

Nice course

par Max W

10 nov. 2018

bit boring

par Roxana C

10 janv. 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.

par Stefano G

1 févr. 2020

Concepts are well explained. Case study is instead confusing and requires additional knowledge and experience (i.e.modelling section).

Sometimes topics are repeated in different sections making it difficult to understand if a task should be completed in a phase or in the next one (i.e. training sets are repeated in both data preparation and modelling).

Lab is not so useful, because it consists in executing python code without a complete understanding.

This course is fundamental to understand the methodology for data science, however I had to look at the videos multiple times to get an overview and I still feel I'm not familiar with it.

par Ivan B

11 juin 2019

Not a useful course overall. The basic premise is fine and logical, but this course did not do a good job differentiating between the different steps involved in the Data Science Methodology and the terminology chosen and used was not explained very clearly or consistently.

Very dry and wordy videos. Example cases used were not straightforward and did not help me understand the concepts that were being conveyed. Good concepts to learn, but this course could have done a much better job at explaining them.

par Oleg N

23 janv. 2020

Thank you for the labs they were great!

Now about everything else:

1. The quality of videos was awfull: the sound was noticeably lower than in previous courses of the specialization,

2. Slides almost irrelevant to text material read, lots of material in such quickly-paced lectures,

3. Lots of medical and mathematical/statistical terms (and other advanced English vocabulary) make this course hard to comprehend to students who rather not that fluent in English.

par Maulik M

7 avr. 2021

Too much theory from the methodology being read out in the videos!!! Needs to be anecdotal and explained practically. The case study taken in the videos also could be simpler. Some concepts like modeling etc that needed to be focused on get the same focus as anything else. There is mention of predictive and descriptive across videos. But this could have been much better sequenced.

But the Jupyter notebooks provided a lot more value than the course itself.

par Rahul S K

15 janv. 2020

I don't feel like I am gaining any knowledge with the help of your course I am just completing it but I dont think after I have completed this course I can tell anybody that I have learnt anything I feel like use less. I cant use this technology anywhere. futhermore if someone asks me whats the use of this IBM watson I am blank i can just play with it thats it nothing else is it helping us somewhere no. what you have to say in this ?

par Nugroho N C

16 janv. 2020

Hard to understanding content in this section. Especially where the tutor give an example of case study. If you want to do some revision fro this course. Please explain it in more general because for people who didnt have Stastic or IT , is not easy to understand. And also for final assignemtn. Could you please make some example how to finish it ? because i dont know to serve the answer like what exactly you want

par Julie H

12 mars 2020

Content was excellent in providing a framework to understand the process. Unfortunately, the tools used were completely inadequate. None of them functioned, course "TA's" frequently said problem was fixed, but it wasn't. Eventually, I just gave up on the ungraded exercises, but that meant I didn't actually learn anything beyond what I could have gotten by reading a book.