Be a current university student or have some university. Have graduated from high school, secondary school, or an equivalent. Be at least 18 years of age by the start of the program. To see a detailed breakdown of what the program may cost you, use our Calculator. Course material and presentation will be at an introductory level. Stanford University sets tuition rates for all students. This course is the required gateway course for the new major in Data Science & Social Systems. In-class exercises and problem sets will provide students with the opportunity to use real-world datasets to discover meaningful insights for policymakers and communities. Lectures will introduce students to the methods of data science and social science and apply these frameworks to critical 21st century challenges, including climate change, educational equity, health policy, and political polarization. Students will also consider the ethical dimensions of working with data and learn strategies for translating quantitative results into actionable policies and recommendations. One cozy bedroom in a multi-unit French-style family guesthouse near Stanford, located just a 5-8min drive to local technology companies. Provides a foundational skill set for solving social problems with data including quantitative analysis, modeling approaches from the social sciences and engineering, and coding skills for working directly with big data. Please note: Full access to Stanford housing. A Summer Session cell phone is provided during on-call shifts. Stanford does not reimburse cell phone costs. Stanford Summer Session provides a 25/month cell phone stipend (12.50 a pay period), which will be paid via Stanford payroll. Introduces students to the interdisciplinary intersection of data science and the social sciences through an in-depth examination of contemporary social problems. Stanford University Summer Conference Services: rents out guest rooms in. All staff are expected to work 20 hours per week (approximately 360 per week). Freshmen and sophomores interested in data science, computing, and statistics are encouraged to attend. The class will start with a brief introduction to R but will move at a relatively fast pace. Some statistical background or programming experience is helpful, but not required. The objectives of this course are to have students (1) be able to connect data to underlying phenomena and think critically about conclusions drawn from data analysis, and (2) be knowledgeable about how to carry out their own data analysis later. Topics covered include introductions to data visualization techniques, summary statistics, regression, prediction, sampling variability, statistical testing, inference, and replicability. Each week consists of three lectures and two labs, in which students will manipulate real-world data and learn about statistical and computational tools. Students will engage with fundamental ideas in inferential and computational thinking. This course will provide a hands-on introduction to statistics and data science. Term withdrawal deadline last day to submit Annulment of Summer Registration to withdraw from the University with a partial tuition refund.
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