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The topics that follow will include machine learning models used for topic modeling, clustering, classification, sentiment analysis, and word embeddings. Students will also be introduced to web scraping. Considerations to both long and short texts of various subject matter. Class examples will be demonstrated primarily in R. This course assumes a bachelors-level background in Statistics or related field and knowledge of R or Python; no prior knowledge of text analysis is assumed.\n\nRobyn Ferg is a senior statistician at Westat. Her doctoral and postdoctoral research focused on developing methods for extracting insights from social media data. She has taught graduate and short courses at the Joint Program in Survey Methodology (University of Maryland) and given talks on text analysis at the Census Bureau, University of Maryland, University of Michigan, Michigan State University, and several national and international conferences. 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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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