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UW Statistics Courses
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== First Year Introduction Sequences == A sequence is typically going to be a 2-3 quarter group of classes that will give you solid basis into statistics. These will all cover probability, introductory statistics, and statistical programming in R, Stata, SPSS, SAS, etc. They should each cover hypothesis testing and statistical inference, descriptive statistics, some visualization, and linear regression. They might go further or touch on other things as well. Many of these sequences will also cover more basic features of quantitative social scientific research like operationalization and measure construction, experiment design, etc. A sequence like is the foundation for quantitative analysis and statistics but it is ''not'' a complete training. In almost all cases, it will likely need to be supplemented with additional classes. All CDSC students should take a sequence during their first year. These are listed more or less in the order of recommendation although different courses will make sense for different students: '''Biostatitics (BIOST)''' Biostats. Applied Biostats II (518) is an excellent course, well-taught, with a tight relationship between theory, method, and application. As of Summer '18, no one in the [[CDSC]] has taken Applied Biostats I (517), but it's this year's recommendation for a first class and we'll update this page when possible. '''Sociology (SOC):''' SOC504, SOC505, and SOC506. These are good courses but are quite applied. For [[CDSC]] members, this would be the easiest minimum option and would be slightly discouraged. '''Communication (COM):''' COM520 + COM521. Some combination of a quantitative research design and basic social scientific epistemology and design. COM520 or COM520 taught by Mako but it is a truly introductory stats class with a strong emphasis on application in GNU R. Like the SOC sequence, this would be discouraged for CDSC folks who would be encouraged to take a more technical course. '''Political Science (POLS):''' This sequence begins with POLS500 in the autumn which is similar to COM520/COM521 and is an introduction to quantitative research in the social sciences. POL501/CS&SS501 focuses on "testing theories with empirical evidence. Examines current topics in research methods and statistical analysis in political science. Content varies according to recent developments in the field and with interests of instructor." POLS503/CS&SS503 is Advanced Quantitative Political Methodology and might be a good choice for a 2nd or 3rd quarter in statistics. It is a slightly mathematical applied statistics class which introduces regression and multi-variable techniques for developing causal arguments using statistics. The course stuck fairly closely to the two textbooks Real Stats and Mastering Metrics (an undergrad textbook) in Spr 2018 and the course sites from the last two years [https://uw-pols503.github.io/2017/][https://uw-pols503.github.io/2018/] are published on GitHub, with instructor notes at [https://jrnold.github.io/intro-methods-notes/] so take a look there if you want a preview of what will be covered. The class is sponsored by Political Science, so some of the content is influenced by their disciplinary norms. '''Economics/Stastics (ECON/STAT):''' If you already have good linear algebra and multivariate calculus, Taking ECON 580 and 581 is a good short-cut to getting a lot of methods covered in other classes. You could take SOC 504,505,506, CS&SS 503,504 560, and 564 or you could take ECON 580, 581 and read a few books. This is the ideal class for any CDSC folks although it will likely be a poor choice until you have a relatively strong mathematics background. Details on these classes are provided below. '''Education Psychology (EDPSY):''' EDPSY490, EDPSY491 strong focus on psychometric techniques drawn from psychology. Should be relatively easier and very applied but will not provide a good training for research using non-experimental settings. Due to the rigor and the focus on experiments, ANOVA, and SPSS, this is discouraged for CDSC members which will typically be dealing with observational data or should, at the very least, build the skills necessary to do so.
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