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返回到 什么是数据科学?

學生對 IBM 提供的 什么是数据科学? 的評價和反饋

4.7
48,217 個評分
9,090 條評論

課程概述

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....

熱門審閱

SH
2021年7月24日

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

RS
2020年5月11日

Very learning experience, I am a beginner in DS, but the instructors in this course simplified the contents that made me I could easily understand, tools and materials were very helpful to start with.

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8801 - 什么是数据科学? 的 8825 個評論(共 9,117 個)

創建者 Sanket B

2019年6月10日

thik hai

創建者 Gloria L

2020年8月25日

Too dry

創建者 Abdelmalek N

2020年2月17日

I

l

i

k

e

i

t

創建者 손승건

2020年1月16日

not bad

創建者 MD S H

2020年11月30日

great

創建者 Luca A

2020年3月1日

Basic

創建者 Antonio S A

2019年11月12日

Basic

創建者 Naman P J

2019年6月1日

basic

創建者 Isak S

2021年4月6日

good

創建者 Muhammad N A K

2020年10月24日

Good

創建者 Arti k b

2020年8月11日

Good

創建者 Sai S L . J

2020年7月10日

GOOD

創建者 Jakub K

2020年5月25日

soso

創建者 Yannick L A

2020年5月8日

Good

創建者 Sudarshan R P

2020年4月29日

Good

創建者 Shaojia W

2020年3月24日

good

創建者 Ashish D

2019年12月22日

Good

創建者 POULOMI S

2019年5月11日

good

創建者 Catherine L

2019年9月30日

V

創建者 Ariel O

2021年8月3日

I​ just finished Google Data Analytics and Google Project Management and got their certifcates. When comparing the teaching styles, I didn't like this IBM course because:

1​. Full of videos and not much reading materials. I am not sure what to take note of, to be honest. Some of them are anecdotal and does not apply to me.

2​. Building from the previous reason, as a non-degree holder, the videos keep on implying that I'm not suited for this career. Compared to Google's courses where there are lots of encouragement and does not make you feel unworthy of this career path. If they want this to be exclusive to degree or masters holders, they should have noted that!

3​. Every quiz is graded. Unlike with Google's where they have practice quizzes.

創建者 Joe W

2019年10月29日

After working as a data scientist looking to add "credentials" based on these certificates, I didn't learn any new concepts. If you have never heard of data science and are in high school or just starting to learn about the field, this course would be useful. Otherwise, if you already have experience I would recommend against taking this course.

創建者 NITYANAND R

2020年6月1日

really very it is very basic thing to understand, what is data science, but it has been very lengthy process to understand this and unnecessarily time was engaged on IBM Cloud Platform, a person who's going to pursue the entire course will definitely use the tools but to introduce the field it should be very short and precise thing

創建者 Lawrence L

2019年7月14日

While I appreciate the talks given by these experts, the way this content was spread out with very little substance was demotivating for me and not an efficient use of time, in my opinion.

It could be much more concise.

I liked the final assignment as a recap and way to interact with my classmates.

創建者 Luigi d M N

2020年5月9日

The course offers some key definitions for what regards data science. The learner should not expect to understand more than just a list of new words related to the topic. The videos and reading material are moderately entertaining and the assessment strategy is excessively straightforward.

創建者 Guillaume T

2020年2月24日

The course consists mostly of generalities and has unfortunately nearly no examples.

Showing a data science project with the difference phases of a project would have been great: problem formulation, data collection, data exploration, analysis, visualization, report preparation, etc.