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

4.7
étoiles
56,474 évaluations

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

SH

24 juil. 2021

Thank you for this coursera.

I 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

PD

18 juil. 2018

I thought this course introduced the topic of data science very well. I think I have a much better idea how to describe data science and common terms associated with the field (like machine learning).

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26 - 50 sur 10,000 Avis pour Qu'est-ce que la science des données ?

par Erik M

3 janv. 2019

Friendly and gentle introduction to data science! Can't wait to go further...

par Robert C

23 mai 2019

Good overview of data science, its history, and uses of data science.

par SOURADIPTA C

31 déc. 2018

It was a good introduction for everyone even novice.

par Shahul H

25 juil. 2021

Thank you for this coursera.

I 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

par Ankit S

30 juil. 2019

I dont appreciate the Peer-graded assessment part. It is simply not acceptable to be reliant on other students for assessment. This is just lazy.

par AKASH P

15 janv. 2022

I throughly enjoyed the course and the fact that everything was explained thoroughly. I always enjoyed Dr. White's personal experience with Data Science as well as other Data Scientists point of view.

par Reinhard H J

9 oct. 2019

I'm sorry to give a low rating, but this course is condescending. Some of the statements are also opinions that contradict things you will hear in other courses, not to mention that a great part of the course is obvious, academic approaches and even how to structure a report like a thesis.

This course added absolutely nothing to my knowledge. You're better off reading a recent blog post, or simply an introduction from a good book on applied ML/data science.

par Yaron R

2 janv. 2020

This course is borderline insulting!

The entire course content could be summaries on half A4 page.

The video clips are interviews with people working in the field. the reading material either reiterate the video clip content or presents a new concept in an elementary school level.

par Sebastian S

28 mars 2019

IBM Cloud advertisement and nothing more.

par Mateusz K

31 déc. 2018

Quite vague and repetetive, I didn't feel like there is a structure in the course, more like a collection of random thoughts by various people. It's more an introduction to what data scientists think about data science than an introduction to data science itself.

par Clayton B H I

12 déc. 2018

This is literally just hype for data science with no substance, include coding from day one or get real, I'm here to learn.

par Weishi W

21 oct. 2018

For what?

par Rick N

27 avr. 2018

I did not learn much from this course. I did not enjoy seeing the young data scientists talking about their jobs. I was not too impressed by Dr. Haider or the professor from NYU.

Dr. Haider misused some words, such as "judgmental" and "argumentative". Without any evidence or examples for support, he claimed that it was more important for a job applicant to have a sense of humor than technical skills.

This course should have named specific techniques used in data science, and how to acquire the knowledge. Regression was mentioned, but the explanation was inadequate. Perhaps the explanation should have been omitted. K-nearest neighbor was mentioned.

Many students want to know what courses to take next, what computer languages to study, etc. What are the computer programming languages of the future?

Students cannot learn everything. Would it make sense for someone to skip some things, and to focus on others? Should everyone learn Python? Does everyone need to learn SQL? What about Tableau? Is that worthless?

How should students set their learning priorities in order to achieve a basic or minimal skill set within 3 or 6 or 12 months?

Remember that most Coursera students already have a college degree.

The course was created several years ago, so I think it needs to be updated regarding developments of the last three years.

par Nathan S

3 janv. 2019

This was a nice and easy course, but the material was quite obvious and not new and could be included in one week lessons of another course.

par Dafydd J

17 janv. 2019

i thought a lot of questions were subjective at aimed at the academic field. It neglected to refer to real world situations and jobs, which is where the majority of learners will surely be coming from. Probably a little bit my own perspective, but I dislike when Data Scientists big them up to be so much better than every other profession and separate themselves from statistics so much (it's part of statistics and should just be considered a branch of such in my opinion, not a separate profession). There was too much of it here.

par Sobhan A

6 mai 2020

Completely waste of time! Very Disappointed with this course. This teaching method is completely inefficient. Some professional people will be interviewed and then you should answer quizzes after each video. It takes more than 3 hours to complete this course, however, all useful information could be provided in 5 slides and discuss them only for 20-30 minutes.

par Patryk W

10 mai 2019

Little value in terms of knowledge or skills for somebody paying for this course. Single Wiki document will cover the entire course content.

par Dharmendra K S

14 août 2019

Descriptive picture of data science. Videos are short but nicely presented which gives an student a clear idea of the subject. Even Documents at the end of the course presentation are well explained.

par Linda T

14 déc. 2019

It is my first time to take an online Coursera course. I am badly grateful for your financial support. too many thanks seem not enough to express my happiness to finish this course about data science

par Akshay B

3 avr. 2019

This is a great course for anyone willing to start exploring the field of Data Science. It starts with basic definitions with proper examples that helps one understand this field with a greater ease!

par Xueting L

27 déc. 2019

This course has only maximum 20% useful content that could have been covered as an executive summary in 10 minutes instead of a whole week. A lot of the content is either fluffy socializing or irrelevant secondary information that has nothing to do with data science. As a toxicologist I'm interested in applying data science to my aggregate exposure estimation, and I have absolutely no interest in knowing the base salary of a data scientist working in North America, or which organization says what (facts do not care about opinions!) but unfortunately this was a mandatory question in the quizzes and was a waste of my time. And I find the self-introduction part of the final assignment a bit surreal and again irrelevant. I come here to learn about data science, not socializing. We have facebook, twitter and instagram for that. What my name is or what I do is irrelevant to others who take this course; where others come from is irrelevant to me; we're wasting our time saying something irrelevant and being forgotten by others in 5 minutes. Hoping to see more tangible content in the next courses.

par Angelique B d l F

27 mars 2019

Very very superficial, most of the course material is about the profile, job and prospects of a data scientist. Hardly any of the content is about the actual science and technology. A few sketchy minutes are spent on Hadoop and deep learning, the rest is fluff. Waste of my time. Too bad, IBM is such a forefront player, I expected a lot more.

par Jan D

5 oct. 2018

Don't take it. No Course Instructors, no help. Not worth the money...

Even the Working Platform is always timing out or has a gateway error.

par Tian Q

15 oct. 2019

In the peer graded assignments, there are always students intentionally click on the lowest score!

par Joel L

22 oct. 2018

Didn't really learn anything but I guess it was a good gatekeeper.