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.
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.
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来自FRAMEWORK FOR DATA COLLECTION AND ANALYSIS的热门评论
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
Excelente curso, genera las herramientas necesarias para la recoleccion, transformacion, analisis e interpretacion de datos que requiero como principiante en este proceso
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.
This great course and a good foundation for the specialization. The lecturer is amazing and experienced. I really enjoyed this one.
This is an excellent introductory course. It provides the learners with all the basic information required to understand surveys.
The deliver papers are great but slides are poor. In overall, a good course to begin learning about Data Collection and Analysis.
Interesting and very useful for work.\n\nI liked it better than a course #5-6. It's not so long, but has many practical insights
I though it was a good background course, however, I wish initially (in week 1) there was more focus on definitions.
very enjoyable. Excellent scene-setter for more in depth treatments of other aspects of survey-led quant research.
Great overview of the survey process. Instructor presented information in a concise manner. Highly recommended.
Frauke is experienced, highly knowledgeable and forward thinking in the theory and practice of survey methods.
This course give sufficient background information to start working on other courses and text about surveys.
The first course in the specialization give comprehesive introduction, good basic concept, good lecture
Helped me to gain a sense of what the survey is and the overall data collection process using surveys
Wonderful beginners guide to the Framework for Data Collection and Analysis. The lecturer was superb!
This is a rare course that teaches data collection framework from the ground up.
Great course within the specialization on survey data collection and analysis
This module provides an excellent framework and introduction to the subject.
Well made course content. One recommendation, please include more examples.
I thought it had just the right level of detail for a foundation course.
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