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

17,150 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

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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1976 - 2000 sur 2,094 Avis pour Data Science Methodology

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.

par Dominik T

4 mai 2020

It was great to learn about the methodology and the process that goes into building a model. However, the video lectures felt like something that was quickly thrown together without any passion; extremely boring with a monotone voice, uninteresting slides, and a core example that was boring and felt uninspired.

par Melissa C

7 févr. 2019

Can't download the transcript for studying. Only get subtitles. A lot of information to learn. Found questions on the tests that were not in the material (I went back through the videos after the tests and no mention of some of the questions). Hope the rest of the courses are more complete.

par Steve O

30 oct. 2018

Sometimes methodology can get verbose and abstract, but this content was quite good. The outline of topics and methodologies could have been a little tighter.The English is not so good and there are lots of spelling and grammar errors. There are also bad links for things like images.

par Frederick A P

15 nov. 2018

The videos contained what felt like a lot of information that would have been bettered digested as a written lesson. I believe the course would have been better if all the information in the videos was also available as a pdf, to really be able to look at the slides while reading.

par Hailu K

23 mai 2021

Doesn't fit as one of the starting courses for the data science certificate series: touching on a lot of jargons that will only be covered later. Plus, without writing the code and performing the analysis one can benefit very limitedly from this course.

par Caner A

12 janv. 2020

The narrator speaks like he is reading the text. Content and especially case study is not easy to understand and somehow the method for teaching makes it more difficult. When you are trying to have full screen, resolution of the pictures are poor.

par Eleni A

8 août 2019

The topic is very interesting, the examples though are not sufficient. It would be helpful to exntend the lesson with many examples, some simple and some complicated, in order to give better knowledge and understanding. It is not very engaging.

par Matthew W C

3 juin 2021

t​he content of this course is not well designed for the beginners, it actually requires a lot of knowledge in data science field in order to fully capture this lecture. however this course is supposed to be an introductory course (I think).

par Max T

13 avr. 2020

The material was not engaging. The quizzes had questions with answer choices that were longer than the actual question and were all correct answers except for the addition, subtraction of one word or in one case a single Roman numeral.

par Wong Y O

9 nov. 2018

The example topic is not easy to understand for people work outside hospital, it is better to use some common examples such as email and credit cards that more easily understand the flow. Too much technical terms and explain too fast.

par Michael F

15 juin 2020

Needlessly complicated, and could be offered in much simpler examples, the expressions and case study is very irritating to understand due to complex description. Also the context of the course could be offered in much simpler way.

par Bivek N

6 déc. 2019

It is a bit too fast for the beginner students to fully grasp the idea. I mean of course, the topics of methodology looks fairly straight forward but the explanation and example used in those topics are not explained in detail.

par Fedrizzi E

4 févr. 2020

The methodology presented is useful, and can be a good reference for those new to the subject. However, it is not always clearly explained, and as in previous courses too many technical terms are employed without definitions.

par Matthew E

20 oct. 2019

The sites have changed so following the directions of this course were very confusing and hard to understand. The update from Data Science Experience to IBM Watson studio did not relate to the video demonstrated in this class

par A L I S O N

15 juin 2019

The final assignment had me ham strung. You are supposed to do each of the 10 steps at the end, with very little guidance on HOW. The there are specific methods shown but what if they don't fit your final assignment example?

par Varun P

13 avr. 2020

I feel that the course is not vary interactive and in some cases it seems to be more like a commentary than an interactive session. Due to that its difficult to understand the basics of some of the important concepts.

par Andrew W

9 oct. 2019

Not as engaging as previous courses. Feels like too much concentration on the healthcare case study, rather than the concepts. More of the key points should be on the slides (for the visual learners among us...)

par Berenice E

8 févr. 2020

No hay suficiente información y el "instructor" no es el mejor en esta sección. En general este apartado fue muy aburrido pero no por el material sino por el tipo de videos sin instructor y solo diapositivas.

par Thinh N

2 oct. 2018

I really like the methodology proposed and introduced in this course. However, the whole idea can be summarized in less than 2 pages, which should be a section (a week) in a data science course (~13 weeks).

par Nathaniel K

5 sept. 2019

Videos get a bit boring. I would prefer less videos and more reading about the methodology. I google IBM Data Science Methodology and got more useful information. Why pay for a course that can be Googled?

par Kiran H S

21 avr. 2020

Case study should have been on simpler example / general topic which everybody would understand or correlate easily. Medical field terminologies / relations would be a black box for most of them

par Walid M

6 mai 2020

i believe the content is targeting more experienced audience, along the course it was bit hard to keep track of all information and the final assessment also aimed for higher level of knowledge

par SG

6 janv. 2020

Complicate course with poor valuation system. It contains a lot of basic information but without detail clarifications. I read external resources for a complex understanding of the material.

par Katarina P

15 juin 2019

The peer review system is just awful. It takes ages to get graded/be able to grade others and the peers might not demonstrate language level required for grading an essay-type assignment.