{"id":1549,"date":"2025-01-17T11:31:05","date_gmt":"2025-01-17T10:31:05","guid":{"rendered":"https:\/\/docenti-deps.unisi.it\/di-biase\/?page_id=1549"},"modified":"2025-01-17T11:39:44","modified_gmt":"2025-01-17T10:39:44","slug":"1549-2","status":"publish","type":"page","link":"https:\/\/docenti-deps.unisi.it\/di-biase\/teaching\/1549-2\/","title":{"rendered":"Data analysis for social scientists 2024\/2025"},"content":{"rendered":"<div id=\"header-1\" class=\" header-hx header-h3\">\n<h3>Learning objectives<\/h3>\n<p>The aim of the course is to lay a foundation for analysis of real-world data.<br \/>\nFirst module: at the end of the module, students will have learned the appropriate skills to analyze complex data.<br \/>\nSecond module: at the end of the module, students will be able to understand methods used in socio-economic research and to apply a range of statistical tools in empirical studies.<\/p>\n<\/div>\n<div id=\"header-2\" class=\" header-hx header-h3\">\n<h3>Requirements<\/h3>\n<p>Elements of descriptive statistics and probability theory.<\/p>\n<\/div>\n<div id=\"header-3\" class=\" header-hx header-h3\">\n<h3>Contents<\/h3>\n<p>The course is organized as follows.<\/p>\n<ul>\n<li>First module (30 hours):<br \/>\nIntroduction to R and RStudio.<br \/>\nData visualization.<br \/>\nData wrangling and tidy data.<br \/>\nGeospatial data and text as data.<br \/>\nStatistical foundations.<br \/>\nPredictive analysis: regression and machine learning.<\/li>\n<li>Second module (30 hours):<br \/>\nCausal inference.<br \/>\nOmitted variables.<br \/>\nMatching techniques.<br \/>\nInstrumental variable models.<br \/>\nRegression Discontinuity Design models.<br \/>\nDifference-in-difference models.<\/li>\n<\/ul>\n<\/div>\n<div id=\"header-4\" class=\" header-hx header-h3\">\n<h3>Methods of evaluation<\/h3>\n<p>Learning assessment will take place through four assignments (two for each module) and a final test.<br \/>\nThe assignments will include exercises and the evaluation of each assignment will weigh 15% of the total.<br \/>\nThe final test, which will weigh 40% of the total, will be a written test with multiple choice questions. The aim of the final test is to verify the understanding of all the statistical techniques addressed during the course.<\/p>\n<\/div>\n<div id=\"header-6\" class=\" header-hx header-h3\">\n<h3>Suggested reading list<\/h3>\n<p>Modern Data Science with R (2nd ed.) by Benjamin S. Baumer, Daniel T. Kaplan and Nicholas J. Horton. Chapman and Hall\/CRC. ISBN-13: \u200e978-0367191498<br \/>\nCausal analysis: Impact evaluation and Causal Machine Learning with applications in R by Martin Huber. MIT Press. ISBN: 9780262545914<\/p>\n<p><a href=\"https:\/\/sbart-unisi.alma.exlibrisgroup.com\/leganto\/public\/39SBART_SBS_MAIN\/lists\/18852262160003297?auth=LOCAL\">https:\/\/sbart-unisi.alma.exlibrisgroup.com\/leganto\/public\/39SBART_SBS_MAIN\/lists\/18852262160003297?auth=LOCAL<\/a><\/p>\n<\/div>\n<div id=\"header-7\" class=\" header-hx header-h3\">\n<h3>More information<\/h3>\n<p>R programming language (R Core team) will be used for data analysis.<br \/>\nE-learning page: <a href=\"https:\/\/elearning.unisi.it\/course\/view.php?id=12059\">https:\/\/elearning.unisi.it\/course\/view.php?id=12059<\/a><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Learning objectives The aim of the course is to lay a foundation for analysis of real-world data. First module: at the end of the module, students will have learned the appropriate skills to analyze complex data. Second module: at the end of the module, students will be able to understand methods used in socio-economic research &hellip; <a href=\"https:\/\/docenti-deps.unisi.it\/di-biase\/teaching\/1549-2\/\" class=\"more-link\">Leggi tutto<span class=\"screen-reader-text\"> &#8220;Data analysis for social scientists 2024\/2025&#8221;<\/span><\/a><\/p>\n","protected":false},"author":66,"featured_media":0,"parent":11,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1549","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/docenti-deps.unisi.it\/di-biase\/wp-json\/wp\/v2\/pages\/1549","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/docenti-deps.unisi.it\/di-biase\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/docenti-deps.unisi.it\/di-biase\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/docenti-deps.unisi.it\/di-biase\/wp-json\/wp\/v2\/users\/66"}],"replies":[{"embeddable":true,"href":"https:\/\/docenti-deps.unisi.it\/di-biase\/wp-json\/wp\/v2\/comments?post=1549"}],"version-history":[{"count":4,"href":"https:\/\/docenti-deps.unisi.it\/di-biase\/wp-json\/wp\/v2\/pages\/1549\/revisions"}],"predecessor-version":[{"id":1555,"href":"https:\/\/docenti-deps.unisi.it\/di-biase\/wp-json\/wp\/v2\/pages\/1549\/revisions\/1555"}],"up":[{"embeddable":true,"href":"https:\/\/docenti-deps.unisi.it\/di-biase\/wp-json\/wp\/v2\/pages\/11"}],"wp:attachment":[{"href":"https:\/\/docenti-deps.unisi.it\/di-biase\/wp-json\/wp\/v2\/media?parent=1549"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}