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.)

History of Statistics In this course we explore how statistical ideas developed historically, beginning with the emergence of data collection in the late 17th century and concluding with modern reflections on big data and AI. Through discussion of primary source material, we examine how key individuals and historical problems culminate in the creation of statistical methods. In the course we recreate historical experiments, analyses, and visualizations using modern tools, gaining insight into how concepts such as uncertainty, error, evidence, and inference evolved over time. We will examine ethical questions that can arise from data collection and analysis and reflect on how historical perspectives inform the use of statistics today.

Regression Theory and Applications (formerly MATH 0211) 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. (Concurrent or prior MATH 0200, and STAT 0116 or STAT 0201 or PSYC 0201 or ECON 0111) (Not open to students who have taken ECON 0211.) 3 hrs lect./disc.

Data Science Across Disciplines In this course, we will gain exposure to the entire data science pipeline—obtaining and cleaning large and messy data sets, exploring these data and creating engaging visualizations, and communicating insights from the data in a meaningful manner. During morning sessions, we will learn the tools and techniques required to explore new and exciting data sets. During afternoon sessions, students will work in small groups with one of several faculty members on domain-specific research projects in Anthropology, Biology, Classics, Political Science, and Statistics. This course will use the R programming language. No prior experience with programming is necessary. (Not open to students who have taken STAT 0118, STAT 0201, ANTH 1230, BIOL 1230, CLAS 1230, ECON 1230, ECSC 1230, ENVS 1230, FMMC 1230, GEOG 1230, HARC 1230, HIST 1230, JAPN 1230, LNGT 1230, MATH 1230, NSCI 1230, PSCI 1230, SOCI 1230, STAT 1230, or WRPR 1230.)  ANTH/LNGT 1230: In this section we will explore indigenous political voice in unexpected places. The data we will analyze will consist of bilingual Apache-English and Maidu-English stories, songs and speeches originally recorded by anthropological linguists in the early twentieth century as examples of traditional culture. Students will use R to search this corpora for how indigenous contributors were also making claims on the future in their address to the researcher and to wider anticipated audiences.  BIOL/ECSC 1230: In this section we will work with data collected by elephant seals equipped with oceanographic instruments in the Southern Ocean. Depending on your interests, you can approach the project from different angles: students focusing on biology will explore where the seals travel and what drives their movements, while those interested in earth science will investigate the temperature and salinity profiles gathered during their dives. Working in teams, you’ll combine these perspectives to build a fuller picture of both seal ecology and the oceanographic processes that shape their environment. Along the way, you’ll practice manipulating and visualizing different types of data including maps of seal tracks, temperature and salinity profiles, and cross-sections of ocean properties. We will also bring in satellite  and autonomous float data to place seal activity and the data they collect in a broader context. By the end, you’ll have a sense of how these different data sources fit together and what unique insights we gain from using seals as oceanographers.  CLAS 1230: In this section students will gain hands-on experience with a variety of natural language processing and text mining techniques by exploring the writings of the ancient historian Plutarch, who lived during the first and second centuries AD.  We will focus on the biographies of "great men" in Plutarch’s Lives, which chronicles the history, morals, and virtues of major figures who played parallel roles in ancient Greek and Roman society. The public domain English translation from Project Gutenberg will serve as our main corpus; however, students with a background or interest in Ancient Greek can work with the text in the original language. Using the R programming language, students will transform unstructured text into quantitative data for statistical analysis and morphosyntactic parsing. We will also apply machine learning models to our data to reveal underlying patterns in large amounts of text. No prior experience with R is necessary.  PSCI 1230: Who votes in elections? Who attends protests? Why? In this section we will use the tools of data science to explore these and other questions about political participation in the Americas. We will examine engagement in different forms of participation and the demographic, economic, social, and other factors that shape participation. The class will introduce students to the basics of survey research and the study of political participation.  Students will complete a final group project showcasing the concepts and tools learned in class.   STAT 1230: In this section students will dive into the world of data science by focusing on invasive species monitoring data. Early detection is crucial to controlling many invasive species; however, there is a knowledge gap regarding the sampling effort needed to detect the invader early. In this course, we will work with decades of invasive species monitoring data collected across the United States to better understand how environmental variables play a role in the sampling effort required to detect invasive species. Students will gain experience in the entire data science pipeline, but the primary focus will be on data scraping, data visualization, and communication of data-based results to scientists and policymakers.

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 Data Science (formerly MATH 0118) 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 MATH 0216) 3 hrs lect./disc.

Meaghan Winder

Reviews and ratings for Meaghan Winder at Middlebury College.Meaghan Winder teaches Intro to Stat and Data Sci, History of Statistics, Regression and more.  Explore top rated instructors and find the best for you.Is Meaghan Winder good ? Find out on MiddCourses.

This class serves its purpose as a segue into the world of statistics as the second class someone should take in the stats department (saying this as an upperclassmen). Many topics covered in regression are covered more in-depth in other courses and Prof. Winder often covered these intersections in stat techniques which was interesting to see. Great course to take!

Professor Winder is very nice and enthusiastic about what she teaches. The class itself is not particularly difficult, but I found it extremely boring. The professor also takes a borderline unacceptable amount of time to grade assignments. For example, the take home portion of our midterm was not graded until finals week. I do not think Professor Winder is a bad teacher, but I would recommend taking this class with another professor.

This course gave a lot of practical knowledge, even though stats isn't my major. I felt that lectures were very fast-paced, overly focused on coding which made the material hard to focus on and retain. Projects were the majority of the grade for the course, 4 throughout the semester with 3 projects assigned in the last 4 weeks. Course felt unbalanced and poorly paced, but not entirely unmanageable.