Probability An introduction to the concepts of probability and their applications, covering both discrete and continuous random variables. Probability spaces, elementary combinatorial analysis, densities and distributions, conditional probabilities, independence, expectation, variance, weak law of large numbers, central limit theorem, and numerous applications.

Statistical Inference       An introduction to the mathematical methods and applications of statistical inference using both classical methods and modern resampling techniques. Topics will include: permutation tests, parametric and nonparametric problems, estimation, efficiency and the Neyman-Pearsons lemma. Classical tests within the normal theory such as F-test, t-test, and chi-square test will also be considered. Methods of linear least squares are used for the study of analysis of variance and regression. There will be some emphasis on applications to other disciplines. This course is taught using R.

Introduction to Statistical and Data Sciences An introduction to statistical methods and the examination of data sets for students with a background in calculus. Topics include descriptive statistics, elementary distributions for data, hypothesis tests, confidence intervals, and regression. Students develop skills in data cleaning, wrangling, visualization, and model fitting using the Statistical Software R.  Emphasis will be placed on reproducibility. (MATH 0121 or equivalent, or by placement.) (Not open to students who have taken MATH 0116, MATH 0118, ECON 0111 (formerly ECON 0210), PSYC 0201, STAT 0116, STAT 0118, BIOL 1230, ECON 1230, ENVS 1230, FMMC 1230, HARC 1230, JAPN 1230, LNGT 1230, NSCI 1230, MATH 1230, SOCI 1230, LNGT 1230, PSCI 1230, WRPR 1230, or GEOG 1230.)

Bayesian Statistics (formerly MATH 0412) In this course, we will learn about the Bayesian paradigm of statistics, in which one’s inferences about parameters or hypotheses are updated as evidence accumulates. The goals of the course include understanding basic concepts of Bayesian inference; deriving posterior distributions; assessing the adequacy of Bayesian models; and effectively communicating results. Topics covered include one-parameter models, conjugacy, and Gibbs samplers. Real-world data and applications will feature heavily in this course. (concurrent or prior STAT 0311) 2.5 hr. lect.

Advanced Hierarchical Modeling (formerly MATH 0712) Hierarchical or multilevel models provide a principled way to model data that are naturally grouped in order to take advantage of the relationship between observations in the same group, but also allow for borrowing of information across groups. In this senior seminar, we will introduce a variety of multilevel models, with a balance between the theoretical and conceptual foundations, as well as implementation and interpretation of the results. This seminar will focus on multilevel linear and logistic models. Every student will write a senior capstone paper.

Regression Theory and Applications Regression is a popular statistical technique for making predictions and for modeling relationships between variables.  In this course we will discuss the theory and practical applications of linear, log-linear, and logistic regression models.  Topics include least squares estimation, coding for categorical predictors, analysis of variance, and model diagnostics.  We will apply these concepts to real datasets using R, a statistical programming language.  (MATH 0200; and MATH 0116 or MATH 0311) 3 hrs lect./disc.

Bayesian Statistics In this course, we will learn about the Bayesian paradigm of statistics, in which one’s inferences about parameters or hypotheses are updated as evidence accumulates. The goals of the course include understanding basic concepts of Bayesian inference; deriving posterior distributions; assessing the adequacy of Bayesian models; and effectively communicating results. Topics covered include one-parameter models, conjugacy, and Gibbs samplers. Real-world data and applications will feature heavily in this course. (MATH 0311) 2.5 hr. lect.

Statistical Learning This course is an introduction to modern statistical, machine learning, and computational methods to analyze large and complex data sets that arise in a variety of fields, from biology to economics to astrophysics. The theoretical underpinnings of the most important modeling and predictive methods will be covered, including regression, classification, clustering, resampling, and tree-based methods. Student work will involve implementation of these concepts using open-source computational tools.

Introduction to Data Science In this course students will gain exposure to the entire data science pipeline: forming a statistical question, collecting and cleaning data sets, performing exploratory data analyses, identifying appropriate statistical techniques, and communicating the results, all the while leaning heavily on open source computational tools, in particular the R statistical software language. We will focus on analyzing real, messy, and large data sets, requiring the use of advanced data manipulation/wrangling and data visualization packages. Students will be required to bring alaptop (owned or college-loaned) to class as many lectures will involve in-class computational activities. (formerly MATH216) 3 hrs lect./disc.

Becky Tang

Reviews and ratings for Becky Tang at Middlebury College.Becky Tang teaches Probability, Statistical Inference, Intro to Stat and Data Sci and more.  Explore top rated instructors and find the best for you.Is Becky Tang good ? Find out on MiddCourses.

Bayes is certainly the hardest course I have taken at Midd so far. That being said, I loved the class and felt that I learned a ton from it. If you are a stats or math major, I would definitely recommend this as an upper-level elective. The beginning of the semester is more homework-heavy, which pretty much had our entire class at office hours consistently. This was hard but we all bonded over it and it became quite a fun learning environment. Later part of the semester was all Case studies and a final project which were fun to work in pairs with real data and apply some of the things we learned. Overall really hard and time consuming, but an amaxing class

I was nervous to take this course but I ended up really liking it. It is a lot of work but if you put in effort and go to office hours/TA hours then it is likely you will succeed. It's a time consuming class as it's 3x a week for 75 mins but I would take it again because I learned so much. 

STAT 201 with professor Tang was not an easy course. While her teaching style is organized and she is very enthusiastic about the content, her grading is extremely tough. I studied for hours and my score on assessments never seemed to reflect that, she is also fairly unwilling to give very many points back despite a low class average. 