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Office
Warner 214
Email
btang@middlebury.edu
Office Hours
STAT 201: M 2-4pm, F 9-10am; FYSE 1077: MF 12-1pm

Courses Taught

Course Description

Citizen Science at Middlebury
Citizen science projects are vehicles for democratizing science, giving ordinary people opportunities to advance scientific knowledge by collecting data, reporting observations, and conducting experiments. In this course students are invited to become citizen scientists by engaging in data collection and analysis around Middlebury. Through reading scientific papers and collecting observational data, we will discover how data quality issues and bias can arise during data collection, thereby impacting the kinds of analyses that can be conducted. We will discuss the origins of citizen science as a concept and explore tensions between “citizen” and “academic” science. Students will complete a semester-long project that contributes to an ongoing citizen science effort at Middlebury. 3 hrs lect./disc.

Terms Taught

Fall 2024

Requirements

CW, SCI

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Course Description

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. (Not open to students who have taken 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.)

Terms Taught

Fall 2022

Requirements

DED

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Course Description

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. (MATH 0118, or MATH 0216, or BIOL 1230, or ECON 1230, or ENVS 1230, or FMMC 1230, or HARC 1230, or JAPN 1230, or LNGT 1230, or NSCI 1230, or MATH 1230 or SOCI 1230) 3 hrs. lect./disc.

Terms Taught

Fall 2022, Spring 2023

Requirements

DED

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Course Description

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. (concurrent or prior MATH 0223 or by waiver) 3 hrs. lect./disc.

Terms Taught

Fall 2023

Requirements

DED

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Course Description

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. (MATH 0310) 3 hrs. lect./disc.

Terms Taught

Spring 2023

Requirements

DED

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Course Description

Advanced 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 APAB 4 or APBC 3, or by waiver) (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.)

Terms Taught

Fall 2024

Requirements

DED

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Course Description

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. (MATH 0310) 3 hrs. lect./disc.

Terms Taught

Spring 2024

Requirements

DED

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Course Description

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. (MATH 0311 or STAT 0311) 2.5 hr. lect.

Terms Taught

Fall 2023

Requirements

DED

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Course Description

Independent Study
Individual study for qualified students in more advanced topics in statistics. Particularly suited for those who enter with advanced standing. (Approval required) 3 hrs. lect./disc.

Terms Taught

Spring 2024, Spring 2025

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Course Description

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. (MATH 311 or STAT 0311; MATH 412 or STAT 0412 suggested)

Terms Taught

Spring 2024

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