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學生對 马里兰大学帕克分校 提供的 Framework for Data Collection and Analysis 的評價和反饋

4.2
548 個評分
132 條評論

課程概述

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....

熱門審閱

MM
2018年3月6日

Very clear and organised structure\n\nClear examples to support learning objectives\n\nAttractive use of voice\n\nOne point for improvement: Do not film while the lecturer is still refinding breath

TJ
2020年10月30日

Useful to build basic knowledge which helps you choosing a better mode and linking the objectives of research with the tools (how).\n\nThanks to the instructor and Coursera.

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126 - Framework for Data Collection and Analysis 的 130 個評論(共 130 個)

創建者 Divya K

2016年8月28日

AWFUL monotonous way of talking. I have such a great interest in this topic, but listening to her makes me want to rip my eyeballs out.

創建者 Samuel F

2020年7月12日

This class provided very little useful information. The lectures were not clear.

創建者 Erika E

2020年12月13日

not not at all

創建者 Raja R G K

2020年9月19日

not good

創建者 Nazeer A

2020年7月29日

Very Bad