class: center, middle, inverse, title-slide # Final Meetup ## DATA 606 - Statistics & Probability for Data Analytics ### Jason Bryer, Ph.D. ### May 12, 2021 --- # Final Exam * Will be available May 19th. * Due by end of day May 23rd. * You may use your book and course materials. * I expect you to complete the exam on your own (i.e. do not discuss with classmates, colleagues, significant others, etc.) * There are two parts: 1. Part one multiple choice questions and short answer questions. 2. Part two has a small data set to analyze with R, then answer some interpretation questions. * Put your answers in the Rmarkdown file and submit the PDF file. **Please do not post your answers online!** --- # Presentations * 8.24 - Peter Gatica * 5.13 - Rathish Sasidharan * Zachary Safir --- # My Work My statistical research interest is in propensity score methods. Propensity score analysis (PSA) is a quasi-experimental design used to estimate causality from observational studies. It is generally conducted in two phases: 1. Estimate propensity scores (i.e. probability of being in the treatment) using the observed covariates. a. Check balance b. Re-estimate propensity scores 2. Estimate effect sizes using typical group differences (e.g. t-tests) See my [Github repository](https://github.com/jbryer/psa) or [Intro to PSA slides](http://epsy887.bryer.org/slides/Intro_PSA.html). Also the PSA Shiny application: ```r psa::psa_shiny() ``` Areas I have worked on: * Multilevel PSA (see [`multilevelPSA`](http://jason.bryer.org/multilevelPSA) R package) * Matching with non-binary treatments (see [`TriMatch`](http://jason.bryer.org/TriMatch) R package) * Bootstrapping PSA (see [`PSAboot`](http://jason.bryer.org/PSAboot) R package) --- # DAACS [The Diagnostic Assessment and Achievement of College Skills](https://daacs.net) (DAACS) is a suite of technological and social supports to optimize student learning. DAACS provides personalized feedback about students’ strengths and weaknesses in terms of key academic and self-regulated learning skills, linking them to the resources to help them be successful students. Applications of Data Science: * We use natural language processing and predictive models to machine score the essays. * We use DAACS data to estimate "risk scores" for students failing so we can target them with resources to help them be successful. Just learned we received $3.8 million grant from the Institute of Education Sciences to test the efficacy at three institutions. Official announcement to come in July. --- # Thank You This has been a great semester. Please don't hesitate to reach out:
Email: [jason.bryer@cuny.edu](mailto:jason.bryer@cuny.edu)
Github: https://github.com/jbryer
Personal Website: https://bryer.org
[LinkedIn](https://www.linkedin.com/profile/view?id=AAMAAATGdnoBOWXg80yqna6fSkgnZdabZP7Ck9w&trk=hp-identity-name)
Twitter: [jbryer](https://twitter.com/jbryer) <br/> You can download all course materials on [Github](https://github.com/jbryer/DATA606Spring2021). Click the [clone or download](https://github.com/jbryer/DATA606Spring2021/archive/master.zip) link to download a zip file.