Introduction to Statistical Science (formerly MATH 0116) A practical introduction to statistical methods and the examination of data sets. Computer software will play a central role in analyzing a variety of real data sets from the natural and social sciences. Topics include descriptive statistics, elementary distributions for data, hypothesis tests, confidence intervals, correlation, regression, contingency tables, and analysis of variance. The course has no formal mathematics prerequisite, and is especially suited to students in the physical, social, environmental, and life sciences who seek an applied orientation to data analysis. (Not open to students who have taken BIOL 0211 or ECON 0111 (formerly ECON 0210) or PSYC 0201)

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.

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 Geography, Political Science, Restorative Justice, or Healthcare. This course will use the R programming language. No prior experience with R is necessary.  MATH/STAT 1230: Students will explore pediatric healthcare data to better understand the risks correlated with various childhood illnesses through an emphasis on the intuition behind statistical and machine learning techniques. We will practice making informed decisions from noisy data and the steps to go from messy data to a final report. Students will become proficient in R and gain an understanding of various statistical techniques.  GEOG 1230: In this section, students will use data science tools to explore the ways migration systems in the United States changed during the COVID-19 pandemic. We will draw on data collected from mobile phones recording each phone’s monthly place of residence at the census tract level. The dataset includes monthly observations from January 2019 through December 2021 allowing the analysis to compare migration systems pre-pandemic with those during the pandemic.  INTD 1230A: Data is a powerful tool for improving health outcomes by making programmatic choices to support justice. In this afternoon section of Data Across the Disciplines, students will be working with Addison County Restorative Justice (ACRJ) on understanding patterns in the occurrence of driving under the influence. ACRJ has over 1,000 cases and would like to better understand their data and come up with ways to access information. We will explore how identity, geography, and support impact outcomes from DUI cases. Using statistical analysis and data visualizations, along with learning about ethical data practices, we will report our findings.   INTD 1230B: Let’s dive into the minutes and reports of local towns to develop an accessible news and history resource. Could this be a tool for small newspapers to track local news more easily? Can we map this fresh data for a new look across geographies? Do you want to help volunteer town officials make decisions and better wrangle with their town’s history and data?  In this course we will develop a focused database of documents produced by several municipal boards and commissions. We will engage in conversation with local officials, researchers, and journalists. This course aims to introduce students to making data from real world documents and the people that make them to generate useful information that is often open but frequently difficult to sift through.  PSCI 1230: How do candidates for U.S. national office raise money? From whom do they raise it? In this section we will explore these questions using Federal Election Commission data on individual campaign contributions to federal candidates. Our analysis using R will help us identify geographic patterns in the data, as well as variations in funds raised across types of candidates. We will discuss what implications these patterns may have for the health and functioning of democracy in the U.S.

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.

Michael Czekanski

Reviews and ratings for Michael Czekanski at Middlebury College.Michael Czekanski teaches Intro to Statistical Science, Introduction to Data Science, DataScience Across Disciplines and more.  Explore top rated instructors and find the best for you.Is Michael Czekanski good ? Find out on MiddCourses.

The class itself is dense. Professor is passionate about the content. It came down for the midterm and final project. He was really available for office hours. You get what you put in. It cool learning about the basic of probability. 

Good at explaining, but goes a little too fast. Projects are pretty light, You can work in groups for most of the so the workload is less and makes the course lighter going forward. Lots of coding, but he's good at explaining what every piece of code means. 

The classes were not actually teaching anything, we were just copying down what he typed. The midterm was clearly way harder than he intended-- he said it should take 45 minutes, and everyone was still there 2 hrs later, but he refused to alter anyone's grades even though we all did bad. Would not take again.