Harris Programming Software

[Elder Raman]

Thesite is secure.

The ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

An adverse foetal environment is associated with increased risk of cardiovascular, metabolic, neuroendocrine and psychological disorders in adulthood. Exposure to stress and its glucocorticoid hormone mediators may underpin this association. In humans and in animal models, prenatal stress, excess exogenous glucocorticoids or inhibition of 11β-hydroxysteroid dehydrogenase type 2 (HSD2; the placental barrier to maternal glucocorticoids) reduces birth weight and causes hyperglycemia, hypertension, increased HPA axis reactivity, and increased anxiety-related behaviour. Molecular mechanisms that underlie the 'developmental programming' effects of excess glucocorticoids/prenatal stress include epigenetic changes in target gene promoters. In the case of the intracellular glucocorticoid receptor (GR), this alters tissue-specific GR expression levels, which has persistent and profound effects on glucocorticoid signalling in certain tissues (e.g. brain, liver, and adipose). Crucially, changes in gene expression persist long after the initial challenge, predisposing the individual to disease in later life. Intriguingly, the effects of a challenged pregnancy appear to be transmitted possibly to one or two subsequent generations, suggesting that these epigenetic effects persist.

Data is everywhere and analytical skills are in high demand by public and private institutions around the world. Over seven weeks, you will gain the skills to retrieve, analyze, and present data through a public policy lens. Learn to use a scientific approach to address today's social issues and create a measurable impact on society.

DPSS equips you with training in data analytics paired with hands-on policy research experience and professional development resources. The program includes three required modules: (1) Data Analytics in Public Policy; (2) Introduction to R Programming; and (3) an immersive Capstone Research Project.

Depending on your primary learning goal, participants can opt for one of the two tracks - the academic track and the professional track. Both tracks provide core foundational courses accompanied by a diverse array of customized resources, ensuring you are ready for your next steps - whether it be getting ready for top-notch graduate programs or preparing for a career pivot.

This course provides an introduction to the statistical foundations, tools, and methods employed by public policy researchers. Explore the fundamental problem of causal inference and learn how to use data, research design, and statistical modeling to navigate around this problem.

This is an introductory course in programming and data analysis for students with no prior coding experience. It begins with foundational basics and progresses to advanced, practical data analytical and visualization skills in R. By the end of the course, you will be able to use R to retrieve, clean and analyze complicated datasets and produce tables, charts and other data visualization tools to convey your findings.

In the capstone research project, you will collaborate with faculty and a group of peers to tackle a real-world issue with datasets, devising solutions and creating deliverables that can enhance your portfolio online.

Academic track participants will harness the skills of research design, policy analysis, and team collaboration to conduct a research project using open-source or faculty-provided datasets. Participants will individually write a research note, showcasing their academic readiness for graduate programs or research jobs.

Professional track participants will dive into real-world datasets with practical analysis and a focus on data visualization. They will produce a policy memo with data visualization (charts, infographics) and get the chance to explore Github to showcase their results online.

The academic track provides a comprehensive toolkit for participants who are interested in further advancing their academic growth by earning a master or PhD-level degree in the social sciences field. Students undertaking this track are required to complete advanced statistics and programming learning such as panel data designs, regression discontinuity, instrument variables etc. of the Data Analytics and Programming in R courses, fostering a comprehensive understanding of advanced content. In addition, as part of the Module 3 Capstone Project, participants will individually write a research note that can be used as a writing sample for one's graduate degree application. Participants will also join live workshops focusing on understanding the academic world and learn tools such as LaTeX to support their future research journey.

The professional track prioritizes applied data analytical skills and techniques of data visualization and storytelling, allowing participants to maximize the application of data analysis in real-world professional settings. In this track, participants only need to complete a portion of the assignments of the Data Analytics and Programming in R courses (Module 1 & 2), providing greater flexibility in their time management. Participants will use the Capstone Project as an immersive learning experience to conduct data storytelling and data visualization practice, and produce a policy memo as a deliverable. In addition, participants will join live workshops focusing on understanding how data skills can be applied in various types of industry jobs, and explore useful resources such as GitHub and QGIS to build the online presence to signal their skillset to future employers.

DPSS alumni have made significant strides in their professional journeys, securing internships, part-time and full-time employment. They've attained diverse positions, including roles as Business Analytics Interns, Data Analyst Assistants, Congressional Interns, Development Coordinators, Financial Analysts, Management Consultants, Software Engineers, and Research Assistants.

Austin Wright is the Assistant Professor at the Harris School of Public Policy and Faculty Director for the Data and Policy Summer Scholar Program, ensuring the holistic curriculum is designed and taught to meet student needs in the UChicago way.

"The goal with our credential programs is to find ways to enable a broader set of learners to have access to the UChicago tools and resources and help them achieve their ultimate goals of improving the world around us."

Jeff Levy is an Assistant Instructional Professor at the University of Chicago Harris School of Public Policy. His background is in applied policy research, working at the intersection of social science and data science.

Jose M. Macias, a current student at Harris, serves as a research associate at the Center for Strategic and International Studies, specializing in quantitative analysis of war and machine learning integration in International Relations. He has previously interned at the U.S. Department of Defense, contributed to The Correlates of War Project, and held fellowships, showcasing a diverse background in cybersecurity, policymaking, and academic achievement.

Alex Sobczynski, a Chicago native and recent graduate from Harris, is a short term consultant at the World Bank and a research assistant in Harris and at UChicago's Development Innovation Lab. Her research is at the intersection of agriculture, conflict, and climate and focuses on household decision making and social networks. With a passion of quantitative methods, her work utilizes satellite spectroscopy and cell phone location data to directly measure outcomes in regions with low state capacity, particularly Afghanistan. Additionally, she has done analysis for the UNHCR and works as a tutor and college consultant.

Once being admitted, participants will secure the seat by submitting a $1,000 USD enrollment deposit, which is non-refundable and applies toward your total program fee. The remaining program fee balance will be paid before the program starts. Detailed payment instructions will be provided to admitted participants via email.

If the participant decides to cancel their Program enrollment, the participant must submit a request in writing at least 30 days prior to the program start date to harriscr...@uchicago.edu to receive the paid amount less the non-refundable enrollment deposit. Requests received 14 to 30 days before the Program start date are subject to a payment of 50% of the Program fee. Requests received within 14 days of the Program start date are subject to full payment of the Program fee. If the participant is unable to join the Program due to circumstances that the participant has no control or influence over, the refund amount will be considered on an individual case-by-case basis.

Explore the DPSS FAQ section for quick answers to common program inquiries. Find valuable information about admissions, scholarships, program fees, and more, ensuring a smooth and informed DPSS experience.

Absolutely! DPSS is designed to accommodate individuals from diverse academic and professional backgrounds, including working professionals, recent graduates, and current students. While it may not be without its challenges in terms of time management, it is certainly manageable. You'll benefit from a dedicated support staff, an exceptional teaching team, and the enriching experience of collaborating with a diverse cohort throughout your journey.

DPSS content is rigorous, but we accommodate participants of all backgrounds. There are no prerequisites for this program. If you're new to data analytics, expect to dedicate approximately 12-15 hours per week. For those who have taken some statistics and/or R programming courses, plan for around 8-12 hours per week. If you already possess a working knowledge of statistical concepts and the R programming language, you can typically commit 6-8 hours per week. Our aim is to provide support and guidance to help you succeed, regardless of your prior experience.

3a8082e126