{ "147603-21901329": { "datetime_modified":"20260409T152812", "datetime_start":"20260721T130000", "datetime_end":"20260721T140000", "has_end_time":1, "date_start":"2026-07-21", "date_end":"2026-07-21", "time_start":"13:00:00", "time_end":"14:00:00", "time_zone":"America\/Detroit", "event_title":"Building and Leveraging Your Network (for Graduate Students)", "occurrence_title":"", "combined_title":"Building and Leveraging Your Network (for Graduate Students)", "event_subtitle":"", "event_type":"Workshop \/ Seminar", "event_type_id":"21", "description":"Building a network and connecting with others is critical to your professional development in graduate school and job\/internship searches. Join this discussion to learn strategies for building your network (including tips on using LinkedIN and the University Career Alumni Network) and how to leverage this network for career exploration and job\/internship searching. There will be plenty of time for your questions! 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The workshop will conclude with information on NSUF user access opportunities and internship pathways at Idaho National Laboratory.", "occurrence_notes":null, "guid":"149277-21906183@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/149277", "building_id":"null", "building_name":"Off Campus Location", "campus_maps_id":"1", "room":"", "location_name":"Virtual", "has_livestream":1, "livestream_type":"other", "livetream_require_login":0, "livestream_id":"", "livestream_link":"", "cost":"", "tags":["chemical engineering","Chemistry","College Of Engineering","Computer Engineering","Computer Science","Computer Science And Engineering","Electrical And Computer Engineering","Electrical Engineering and Computer Science","Energy","Engineering","Materials Science","Mechanical Engineering","Michigan Engineering","Michigan Physics","Nuclear Engineering and Radiological Sciences"], 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Registration and payment are required a minimum of two weeks prior to the start of the course.\n\nFounded in 1948, the Summer Institute in Survey Research Techniques is designed specifically to meet the needs of professionals and graduate students seeking to deepen their expertise in survey methodology and data collection. Offered through the Michigan Program in Survey and Data Science within the Institute for Social Research at the University of Michigan, the program provides a rigorous and flexible curriculum that blends theoretical foundations with practical application \u2014 entirely online.\n\nSampling in Practice\n\nUnlocking the art and science of sampling with an applied, hands-on approach, the course Sampling in Practice is designed for applied practitioners who want to master real-world sampling techniques through active learning and practical programming. Students will learn about probability sampling methods, including simple random sampling, stratification, systematic selection, cluster sampling, probability proportional to size sampling, and multistage sampling. We will also cover sampling cost models, sampling error estimation techniques, non-sampling errors, missing data, and nonprobability samples. The course emphasizes practical implementation, featuring interactive coding exercises and in-class examples to reinforce each concept. A culminating project will give students the opportunity to integrate multiple techniques into a comprehensive sample design and demonstrate the profession in designing surveys, selecting subjects, analyzing sample data, and solving real sampling problems using modern statistical tools.\n\nWhy take this course? \n\nThe course is crafted for students and practitioners eager: \n\nTo build proficiency in modern sampling techniques through active engagement and practical coding experience\nTo understand the basic ideas, concepts and principles of probability sampling from an applied perspective\nTo be able to identify and appropriately apply sampling techniques to survey design problems\nTo understand and be able to assess the impact of the sample design on survey estimates\nTo be able to compute the sample size for a variety of sample designs\nTo learn how to design and select a probability sample involving complex sampling techniques in a survey project, and receive expert feedback on a sampling report. \n\nYajuan Si is a Research Associate Professor in the Michigan Program in Survey and Data Science, located within in the Institute for Social Research at the University of Michigan. She holds a Ph.D. in statistical science from Duke and received postdoctoral training at Columbia. Yajuan\u2019s research focuses on methodology development, from data analysis to study design, in streams of Bayesian statistics, linking design- and model-based approaches for survey inference, data integration, missing data analysis, confidentiality protection, and causal inference, with applications in the social and health sciences. 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