This graduate course will cover the modeling and computation required to perform advanced data analysis from the Bayesian perspective. Visit coronavirus.upenn.edu, the University's dedicated coronavirus COVID-19 web page, for the latest updates. Poisson processes, including extensions such as non-homogeneous, compound, and mixed Poisson processes are studied in detail. It delves into classification methodologies such as logistic regression. Theory of the Gaussian Linear Model, with applications to illustrate and complement the theory. STAT613 Regression Analysis for Business. This course may be taken concurrently with the prerequisite with instructor permission. By the end of the course the student will be familiar with and have applied all these tools and will be ready to use them in a work setting. The interpretation of regression models within the context of applications will be stressed, presuming knowledge of the underlying assumptions and derivations. Estimation, with a focus on properties of sufficient statistics and maximum likelihood estimators. This course does not have business applications but has significant overlap with STAT101 and 102. Course includes topics from natural language processing (NLP), such as identifying parts of speech, parsing sentences (e.g., subject and predicate), and named entity recognition (people and places). Do gun control laws cause more or less murders or have no effect? Both theory and applications will be stressed. Prerequisites: Must be a declared Statistics Concentrator or Business Analytics Concentrator or Statistics Minor or Data Science Minor. Potential topics include: empirical process theory; online learning; stochastic optimization; margin based algorithms; feature selection; concentration of measure. Unsupervised techniques suited to feature creation provide variables suited to traditional statistical models (regression) and more recent approaches (regression trees). Introduction to concepts in probability. Topics include system estimation with instrumental variables, fixed effects and random effects estimation, M-estimation, nonlinear regression, quantile regression, maximum likelihood estimation, generalized method of moments estimation, minimum distance estimation, and binary and multinomial response models. Questions about cause are at the heart of many everyday decisions and public policies. STAT955 Stochastic Calculus and Financial Applications. Recent exposure to the theory and practice of regression modeling is recommended. The course reviews statistical hypothesis testing and confidence intervals for the sake of standardizing terminology and introducing software, and then moves into regression modeling. Rates of convergence. Moments and moment generating functions. Continuation of STAT101. Graphical displays; one- and two-sample confidence intervals; one- and two-sample hypothesis tests; one- and two-way ANOVA; simple and multiple linear least-squares regression; nonlinear regression; variable selection; logistic regression; categorical data analysis; goodness-of-fit tests.