Framework for Data Collection and Analysis

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课程主页: https://www.coursera.org/archive/data-collection-framework

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课程简介

University of Maryland, College Park

课程大纲

The first course in the specialization provides an overview of the topics to come. This module walks you through the process of data collection and analysis. Starting with a research question and a review of existing data sources, we cover survey data collection techniques, highlight the importance of data curation, and some basic features that can affect your data analysis when dealing with sample data. Issues of data access and resources for access are introduced in this module.

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课程详情

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.

数据收集和分析框架:本课程将为您提供有关现有数据产品的概述,并对数据收集情况有一个很好的理解。在各种示例的帮助下,您将学习如何识别哪些数据源可能与您的研究问题相匹配,如何将研究问题分解为可衡量的部分以及如何考虑分析计划。此外,本课程将为您提供一个通用框架,使您不仅可以了解成功收集和分析数据所需的每个步骤,还可以帮助您识别与不同数据源相关的错误。您将学习一些度量标准以量化每个潜在的错误,因此您将拥有描述数据源质量的工具。最后,我们将介绍私营企业和政府机构所做的各种大规模数据收集工作,并通过这些示例回顾所学到的概念。本课程适合初学者以及知道一个特定数据源的人,但不适合其他人,并且正在寻找评估数据产品的通用框架。

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