CURSO:FUNDAMENTOS DE METODOLOGIA DE ENCUESTAS Y CIENCIA DE DATOS TRADUCCION:FUNDAMENTALS OF SURVEY METHODOLOGY AND DATA SCIENCE SIGLA:SOL4033 CREDITOS:10 MODULOS:02 CARACTER:OPTATIVO TIPO:CATEDRA CALIFICACION:ESTANDAR DISCIPLINA:SOCIOLOGIA PALABRAS CLAVE:METODOLOGIA DE ENCUESTAS, CIENCIA DE DATOS NIVEL FORMATIVO:MAGISTER I.DESCRIPCIÓN DEL CURSO In this course the student will learn a set of principles of survey and data science that are the basis of standard practices in these fields. The students will be exposed to key terminology and concepts of collecting and analyzing data from surveys and other data sources to gain insights and to test hypotheses about the nature of human and social behavior and interaction. The course will also present a framework that will allow the student to evaluate the influence of different error sources on the quality of data. II.OBJETIVOS DE APRENDIZAJE 1.Be able to apply the key terminology used by survey methodologists and data scientists. 2.Be able to assess the quality of data from different sources based on a data quality framework. 3.Be able to select an appropriate data source to answer different types of research questions. 4.Be able to understand the influence of coverage, sampling, and nonresponse on data quality and know how to deal with deficiencies of the data. 5.Be able to clearly identify the steps involved in data preparation, data processing, data analysis, and data visualization. 6.Be able to comply with ethical standards in survey research and data science. III.CONTENIDOS 1.Introduction to data science 2.Total survey error framework 3.Data Quality 4.Coverage Error in surveys 5.Modes of data collection 6.Data generation from other sources 7.Survey sampling 8.Questionnaire design 9.Survey interviewing 10.Unit and Item Nonresponse 11.Data processing 12.Data analysis and visualization IV.METODOLOGIA PARA EL APRENDIZAJE -Flipped-classroom -Lectures delivered through pre-recorded online video sessions -Live meetings, via a web platform, with discussions -Quizzes and Homeworks -Readings V.EVALUACION DE APRENDIZAJES -Online quizzes (60%) -Online final open-book exam: 30% -Class participation in online meetings and forum: 10% VI.BIBLIOGRAFIA Required readings (books) Groves, R.M., Fowler, F.J. Jr., Couper, M.P., Lepkowski, J.M., Singer, E., & Tourangeau, R. (2009). Survey Methodology, Second Edition. New York: Wiley. Peng, R.D. & Matsui, E. (2015). The Art of Data Science. A Guide for Anyone Who Works with Data. Leanpub. (available online at https://leanpub.com/artofdatascience). Required readings (articles) Leek, J.T. and Peng, R.D. (2015). What is the question? Science, 347, 1314-1315. Biemer, P. (2010). Total Survey Error. Design, implementation, and evaluation. Public Opinion Quarterly, 74, 817-848. Hargittai, E. (2015). Is bigger always better? Potential biases of Big Data derived from social network sites. The Annals of the American Academy of Political and Social Science, 659, 63-76. Kreuter, F. and Peng, R.D. (2014). Extracting information from Big Data: Issues of measurement, inference and linkage. In J. Lane, V. Stodden, S. Bender, and H. Nissenbaum (Eds.), Privacy, Big Data, and the Public Good: Frameworks for Engagement, 257-275. Boyd, D. & Crawford, K. (2012). Critical questions for Big Data. Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Sociology, 15, 662-679. Lazar, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: Traps in Big Data analysis. Science, 343, 1203-1205. Wells, C. & Thorson, K. (2017). Combining big data and survey techniques to model effects of political content flows in Facebook. Social Science Computer Review, 35, 33-52. Baker, R. et al. (2010). AAPOR report on online panels. Public Opinion Quarterly, 74, 711-781. Foster, I. & Heus, P. (2016). Databases. In I. Foster, R. Ghani, R.S. Jarmin, F. Kreuter, & J. Lane (Eds.), Big Data and Social Science: A Practical Guide to Methods and Tools. Chapman & Hall, 93-124. Yalcin, A. & Plaisant, C. (2016). Information Visualization. In I. Foster, R. Ghani, R.S. Jarmin, F. Kreuter, & J. Lane (Eds.), Big Data and Social Science: A Practical Guide to Methods and Tools. Chapman & Hall, 243-263. Barocas, S. & Nissenbaum, H. (2014). Big Data?s end run around anonymity and consent. In J. Lane, V. Stodden, S. Bender, & H. Nissenbaum (Eds.), Privacy, Big Data, and the Public Good: Frameworks for Engagement, 44-75. Additional required and recommended readings will be made available on the course website. None of the information in the recommended readings will be included on a homework assignment or the final exam. PONTIFICIA UNIVERSIDAD CATOLICA DE CHILE INSTITUTO DE SOCIOLOGIA / NOVIEMBRE 2018