- Data Collection
- Cluster Sampling
- R Programming
- Missing Data
- Proceso de generación de datos
- Recopilación de datos
- Calidad de los datos
- análisis de datos
Survey Data Collection and Analytics 專項課程
Collect and analyze data, and communicate results. Learn to collect quality data and conduct insightful data analysis in six courses.
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您將獲得的技能
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無需相關領域的預備知識無需相關經驗。
無需相關領域的預備知識無需相關經驗。
專項課程的運作方式
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Coursera 專項課程是幫助您掌握一門技能的一系列課程。若要開始學習,請直接註冊專項課程,或預覽專項課程並選擇您要首先開始學習的課程。當您訂閱專項課程的部分課程時,您將自動訂閱整個專項課程。您可以只完成一門課程,您可以隨時暫停學習或結束訂閱。訪問您的學生面板,跟踪您的課程註冊情況和進度。
實踐項目
每個專項課程都包括實踐項目。您需要成功完成這個(些)項目才能完成專項課程並獲得證書。如果專項課程中包括單獨的實踐項目課程,則需要在開始之前完成其他所有課程。
獲得證書
在結束每門課程並完成實踐項目之後,您會獲得一個證書,您可以向您的潛在雇主展示該證書並在您的職業社交網絡中分享。

此專項課程包含 7 門課程
Framework for Data Collection and Analysis
Este curso te proporcionará una visión general de los productos de datos existentes y una buena comprensión del panorama de recopilación de datos. Con la ayuda de varios ejemplos, aprenderás cómo identificar qué fuentes de datos probablemente coincidan con tu pregunta de investigación, cómo convertir tu pregunta de investigación en piezas medibles y cómo pensar en un plan de análisis. Además, este curso te proporcionará un marco general que no solo te permitirá comprender cada paso requerido para una recopilación y un análisis de datos exitosos, sino que también te ayudará a identificar los errores asociados con diferentes fuentes de datos. Aprenderás algunas métricas para cuantificar cada error potencial y, por lo tanto, tendrás herramientas disponibles para describir la calidad de una fuente de datos. Finalmente, presentaremos diferentes esfuerzos de recopilación de datos a gran escala realizados por agencias gubernamentales y del sector privado, y revisaremos los conceptos aprendidos a través de estos ejemplos. Este curso es adecuado tanto para principiantes como para aquellos que conocen una fuente de datos en particular, pero no otras, y buscan un marco general para evaluar productos de datos.
Data Collection: Online, Telephone and Face-to-face
This course presents research conducted to increase our understanding of how data collection decisions affect survey errors. This is not a “how–to-do-it” course on data collection, but instead reviews the literature on survey design decisions and data quality in order to sensitize learners to how alternative survey designs might impact the data obtained from those surveys.
社会调查的问卷设计
This course will cover the basic elements of designing and evaluating questionnaires. We will review the process of responding to questions, challenges and options for asking questions about behavioral frequencies, practical techniques for evaluating questions, mode specific questionnaire characteristics, and review methods of standardized and conversational interviewing.
Sampling People, Networks and Records
Good data collection is built on good samples. But the samples can be chosen in many ways. Samples can be haphazard or convenient selections of persons, or records, or networks, or other units, but one questions the quality of such samples, especially what these selection methods mean for drawing good conclusions about a population after data collection and analysis is done. Samples can be more carefully selected based on a researcher’s judgment, but one then questions whether that judgment can be biased by personal factors. Samples can also be draw in statistically rigorous and careful ways, using random selection and control methods to provide sound representation and cost control. It is these last kinds of samples that will be discussed in this course. We will examine simple random sampling that can be used for sampling persons or records, cluster sampling that can be used to sample groups of persons or records or networks, stratification which can be applied to simple random and cluster samples, systematic selection, and stratified multistage samples. The course concludes with a brief overview of how to estimate and summarize the uncertainty of randomized sampling.
提供方

马里兰大学帕克分校
The University of Maryland is the state's flagship university and one of the nation's preeminent public research universities. A global leader in research, entrepreneurship and innovation, the university is home to more than 37,000 students, 9,000 faculty and staff, and 250 academic programs. Its faculty includes three Nobel laureates, three Pulitzer Prize winners, 47 members of the national academies and scores of Fulbright scholars. The institution has a $1.8 billion operating budget, secures $500 million annually in external research funding and recently completed a $1 billion fundraising campaign.

密歇根大学
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
常見問題
退款政策是如何规定的?
我可以只注册一门课程吗?
有助学金吗?
我可以免费学习课程吗?
此课程是 100% 在线学习吗?是否需要现场参加课程?
完成专项课程后我会获得大学学分吗?
完成专项课程需要多长时间?
What background knowledge is necessary?
Do I need to take the courses in a specific order?
Will I need to retake the Questionnaire Design course if I completed it previously
What will I be able to do upon completing the Specialization?
還有其他問題嗎?請訪問 學生幫助中心。