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.
Acerca de este Curso
Habilidades que obtendrás
Universidad de Maryland en College Park
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.
- 5 stars53,30 %
- 4 stars26,27 %
- 3 stars12,61 %
- 2 stars3,60 %
- 1 star4,20 %
Principales reseñas sobre FRAMEWORK FOR DATA COLLECTION AND ANALYSIS
Lot of definitions, so some of the questions (especially week 4) test reading comprehension more than understanding - but hopefully this will improve in the subsequent courses of the specialization.
Frauke is experienced, highly knowledgeable and forward thinking in the theory and practice of survey methods.
very enjoyable. Excellent scene-setter for more in depth treatments of other aspects of survey-led quant research.
This is an excellent introductory course. It provides the learners with all the basic information required to understand surveys.
Acerca de Programa especializado: Survey Data Collection and Analytics
This specialization covers the fundamentals of surveys as used in market research, evaluation research, social science and political research, official government statistics, and many other topic domains. In six courses, you will learn the basics of questionnaire design, data collection methods, sampling design, dealing with missing values, making estimates, combining data from different sources, and the analysis of survey data. In the final Capstone Project, you’ll apply the skills learned throughout the specialization by analyzing and comparing multiple data sources.
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