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学生对 University of Maryland, College Park 提供的 Framework for Data Collection and Analysis 的评价和反馈

4.2
766 个评分

课程概述

This course will provide you with an overview over existing data products and a good understanding of the data collection landscape. With the help of various examples you will learn how to identify which data sources likely matches your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. Furthermore this course will provide you with a general framework that allows you to not only understand each step required for a successful data collection and analysis, but also help you to identify errors associated with different data sources. You will learn some metrics to quantify each potential error, and thus you will have tools at hand to describe the quality of a data source. Finally we will introduce different large scale data collection efforts done by private industry and government agencies, and review the learned concepts through these examples. This course is suitable for beginners as well as those that know about one particular data source, but not others, and are looking for a general framework to evaluate data products....

热门审阅

MM

Mar 6, 2018

Very clear and organised structureClear examples to support learning objectivesAttractive use of voice One point for improvement: Do not film while the lecturer is still refinding breath

KM

Oct 4, 2018

The teacher for the course was great. She explained everything very clearly. She also explained what is coming next. Learned a lot. Reading materials were overwhelming.

筛选依据:

76 - Framework for Data Collection and Analysis 的 100 个评论(共 172 个)

创建者 Hassana R A

Aug 16, 2022

excellent

创建者 Srinivas B

Nov 10, 2016

Excellent.

创建者 Ahmet D S

Aug 31, 2022

Wonderful

创建者 Pratibha k

Mar 16, 2022

excellent

创建者 Deleted A

Jan 6, 2022

very nice

创建者 UMORU B

Jul 28, 2021

Very Good

创建者 Eshak B

May 8, 2017

thank you

创建者 Musharavati E M

Feb 25, 2019

perfect

创建者 Romulo N U

Aug 18, 2017

Compreh

创建者 Cris C M

Nov 13, 2016

Thanks

创建者 CHIRRI M

Nov 25, 2024

nice

创建者 温皓意 W E

May 29, 2023

good

创建者 TAVVA R S R

Mar 21, 2023

GOOD

创建者 Muzaffer E

Jul 26, 2022

goşt

创建者 IMMANVEL J

Jul 22, 2022

good

创建者 NAGALADINNE G B B

Dec 20, 2021

good

创建者 RAHUL P

Sep 2, 2020

Good

创建者 JOSHUA E

Feb 25, 2019

BEST

创建者 Sabiha E B

Jun 21, 2022

m

创建者 Karene G

Dec 28, 2016

I

创建者 Simon B

Jul 1, 2018

A lot of useful and interesting content!The quiz questions are sometimes poorly worded - missing words, bad grammar, sometimes illogical. The switching between video and slides is off-putting for me, especially if you are trying to take notes. Can the screen not be split? What do other coursera courses do?The curtain backdrops seems rather too 'staged'. On the whole - good.

创建者 Ross G

Jan 7, 2018

Overall I found the course to be really useful, and I feel confident moving into the next module. I would have benefitted from having more practical examples given to help some of the terminology sink in a little better, but I think that speaks more to my unfamiliarity with the language than with the lecturer's methods.

创建者 Ellen W

Mar 24, 2019

The last quiz was terrible. I watched the classes, read the transcripts, and still had a hard time with it. Then I wasted $300 because i got sidetracked and it as so unpleasant to return to! (My fault, but still....)

Maybe worth re-tooling that quiz, as the rest of the class was quite good.

创建者 Saif U K

Jul 20, 2016

An entremely good effort, gives you a wonderful introduction to the wonderful world of Survey Research in general and data collection overall. One of the plus point is that it directs you towards quite a few new sources of data that is pretty interesting if you are an active researcher.

创建者 Adriana B

Aug 29, 2019

I had a lot of work with this course. I'm working in a organization of Statistics and I don't have much time to study. Sometimes, I had to study during my work and it is not good. I'm not sure that the hours spent on the course were correctly estimated by the University staff.