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Avis et commentaires pour d'étudiants pour Qu'est-ce que la science des données ? par IBM

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55,027 évaluations
10,389 avis

À propos du cours

The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today....

Meilleurs avis

BB

21 févr. 2019

Excellent quality content! It's a great introductory course that really gets you interested in Data Science. I would highly recommend it to anyone curious in learning about what Data Science is about.

SH

24 juil. 2021

Thank you for this coursera.\n\nI get know experience and knowledge in using different kinds of online tools which are useful and effective. I'll use some of them during my lessons. And lots of thanks

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51 - 75 sur 10,000 Avis pour Qu'est-ce que la science des données ?

par Preston K

1 oct. 2018

Utter waste of time

par Andrew F

3 janv. 2019

Great introduction to Data Science!

par Vincent Z

7 janv. 2019

This is really an introductory course, and there is not much to be learned, not a single line of programming or a single chart generated. But it can all be done in a single day, so it is a necessary evil to reach the good stuff in the specialization, I guess.

par Nicholas B

2 févr. 2020

Extremely basic introductory course. Unfortunately you don't learn much about actual data science methods. Quiz questions tend to require you to memorize word for word quotations of supplied text, as opposed to challenging you to think about concepts. I would recommend this course for someone completely new to the idea of data science, but not to people who already know a bit.

par Shelley

23 sept. 2018

The course provides a good overview of data science in general. I particularly liked the definitions of a data scientist and data science. Mr. Haider's definitions are inclusive, broad and encouraging, He says one of the most important traits for a data scientist to possess is curiosity and that tools and techniques can be learnt.

The course also touches upon hot topic areas that people have heard of but most do not understand - i.e. Machine Learning, Neural Networks, Data Mining and Big Data. I have a much better idea what these terms mean now along with the tools of the trade. The course was quite short and concise. I found it the perfect pace for me. The quizzes matched the content and there was nothing extraneous.

I am looking forward to the other courses in the specialization. A quick glance has shown me that the difficulty level increases quite a lot in the other courses and I would definitely have to invest a lot more time in them. The start has been gentle and encouraging, thank you!

par Enas J k

22 juil. 2020

This course has very detailed information on data science and data scientists. The real-life examples and applications of data science presented by different data scientists are also amazing. Overall an excellent course for anyone who wants to venture into this amazing field.

par Abdul W

31 mai 2020

After completing this course you can easily understand and define what is Data Science and clear your doubt about Data Science.I recommend this course to all beginners.

par longmen

6 mai 2019

I have learnt about what the data science is and it's basic knowledge. I am glad I took the course. I will continue finishing the rest of the courses.

par Kanchan P

3 janv. 2019

This is a very good introduction to what actually is data science! Lot of people gets really confused with the definitions and area!

par Sergi

1 janv. 2019

Direct to the point. Increases one's passion to study Data Science by summarizing the main topics. Simple and brilliant

par Amarjot S

7 mars 2020

This course equips a person with all necessary knowledge required to get started in this field with confidence.

par uzair k

7 mars 2020

A very brief and complete introduction of Data Science from industry experts highly recommended course

par Mahesh K

3 janv. 2019

It encompasses fine details to introduce data science and explore data scientists as a career.

par Harsh R

1 juin 2020

Amazing course to a roadmap to data science

par Ferry T

20 août 2019

Great for introduction!

par Irfani K

25 nov. 2020

Very good thank you

par Chan H D L

3 janv. 2019

Very informative and presented by respected individuals with a passion for the field. The only critique is that the material might be a little outdated as it seems to have been created around 2014-2015.

par Dwight F

1 janv. 2019

It does in fact answer a basic, fundamental question; what is Data Science?

par Surawut P

10 mai 2022

The content is good and easy to follow.

What I hate about this course the most is all test, quiz and examimation.

Most of their questions are not fair. They require to recite inconsequencial minor detail, such as who or which book said what.

I expect the test to recall about main concept, such as "What is different between AI, ML, and deep learning?", "What is properties of big data?", "what is application of regression". These kind of questions recall things much more important than minor detail I mention above, but they are non existent.

This happen possibly because the questions emphasized too much on module articles, which is full with detail, rather than clip videos, which present important concepts.

I hope you to revise examination questions to be more appropriate. I feel frustrate when doing them because asking minor detail feel like you are cheating upon students.

par Steven G

23 mai 2021

I genuinely enjoyed this course, but the quizzes are absolutely irrelevant and petty to the point of absurdity. How is attributing a quote to Hal Ronald Varian going to make me a better data scientist? How is know the specifics of one person's research about houses relevant in the massive field of data science? Your quizzes need to focus on key concepts instead of minutiae

par K M

29 juil. 2021

It's a decent course if you don't know why you want to go into data science but if you have an idea, then it's just listening to other people talk about why they like the field without teaching you much.

par Anna R

17 févr. 2021

Broad review of the definition of data science. Can easily get the same information from a quick Google search. Week 3 was the most useful.

par Roger A

26 juil. 2020

Many interviews, nice chats, but not so much content. I was expecting some more theory/practice, not so much documentary.

par Ross E

25 mars 2020

Most of the transcripts of the videos were from old or different versions of the videoes. This fails the basic principle one of the core of the five Vs: Veracity. None done.

There were countless errors in the IBM voiced-over, animation videos. For example saying that data mining is "automated" when it was just explained that data priming - which is often highly manual at the outset - is an important part of the first steps of data mining. It is absolutely NOT inherently an automated process from end to end.

The final "capstone" assignment which was essentially regurgitation was graded incorrectly. Especially with respect to the final reading. Students were asked to list the "main" sections of what should constitute a report to be given to stakeholders following data science based research. Firstly, dictating sections is stupid as you need to customise to your audience and NO, doing it that way should never be prescribed as universal. Secondly, even adhering strictly to what the reading said and ONLY what the reading said, the grading criteria was WRONG. How on earth did you list Appendices, CLEARLY stated as OPTIONAL as one of the 10 main sections? Not only that, you listed sub-sections as whole sections. For students that got the answer correct, I graded them as such and commented that I'm doing this because the criteria was in fact erroneous.

It's one MOOC. How hard is it to get the basics right? What happened to the IBM culture that used to make software engineers write all their code without a compiler to MAKE SURE what they were building was as correct as possible before compiling because of a focus on quality?

Amateur hour over here. Not inspiring.

par SHANNON L H

12 sept. 2019

Was pretty upset that the answers on the final assignment were incorrect according to the course materials. I am an OCD person that is very by the book, who studies and seeks my answers directly from the materials. I am hear to learn and depend on you to have accurate learning materials and tests that follow the course materials.

I create procedure manuals for staff. One of the first things I do when I finish a new manual, is go through each thing, step by step, to make sure it is accurate. My manuals are for a handful of people, your learning materials are for thousands of people, many of which have language barriers, as English is not their first language. So this makes it even more important for your assignments/tests to be extremely clear in their questions and the answers correct according to the course material. When you have a tremendous amount of complaints about this on the discussion forums and no one of power is doing anything to correct this, this is a major issue.

I had started a specialization with Coursera a few months before and quit near the end of course two due to extreme frustration with these same issues. I love the idea of these specializations, and would love to take many of them. I hope and pray that the rest of this course will be a vast improvement over the last assignment in Course 1 week 3.