Plant Community Ecology This course will explore the structure and dynamics of plant communities, with a particular emphasis on temperate forest communities. We will investigate patterns in community diversity and structure, explore how plant populations and plant communities respond to environmental disturbances, and investigate the effects of anthropogenic influences (climate change, introduced species, habitat conversion) on plant communities. Labs will emphasize fieldwork at local research sites, and will provide exposure to techniques of experimental design in plant ecology and basic approaches to describing plant community structure and dynamics.

Experimental Design and Statistical Analysis Experimental design is one of the most important parts of doing science, but it is difficult to do well. How do you randomize mice? How many replicate petri plates should be inoculated? If I am measuring temperature in a forest, where do I put the thermometer? In this course students will design experiments across the sub-areas of biology. We will run student designed experiments, and then learn ways to analyze the data, and communicate the results. Students planning to do independent research are encouraged to take this course. (BIOL 0140 or BIOL 0145).

Biostatistics II In this course we will explore how to use statistics to help answer questions in biology, building on the foundation set in Biology 211. There will be a focus on real-life data sets which require additional statistical complexity, for example accounting for non-normally distributed data or fixed versus random effects. We will think about the philosophical framework behind statistical methods, particularly considering the recent critique of an over-reliance on p-values. The class will have a “hands-on” approach in which we implement examples of covered material in the statistical programming language R.

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 Biology, Geography, History, Mathematics/Statistics and Sociology. This course will use the R programming language. No prior experience with R is necessary.  BIOL 1230: Students enrolled in Professor Casey’s (Biology) afternoon section will use the tools of data science to investigate the drivers of tick abundance and tick-borne disease risk. To do this students will draw from a nation-wide ecological database.  GEOG 1230: In this section, we will investigate human vulnerability to natural hazards in the United States using location-based text data about hurricane and flood disasters from social media. We will analyze data qualitatively, temporally, and spatially to gain insights into the human experience of previous disasters and disaster response. We will present findings using spatial data visualizations with the aim of informing future disaster preparedness and resilience.  HIST 1230: In U.S. history, racial differences and discrimination have powerfully shaped who benefited from land and farm ownership. How can historians use data to understand the history of race and farming? Students will wrangle county- and state-level data from the U.S. Census of Agriculture from 1840-1912 to create visualizations and apps that allow us to find patterns in the history of race and land, to discover new questions we might not know to ask, and to create tools to better reveal connections between race, land, and farming for a general audience.    STAT 1230: In this course 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.  SOCI 1230:  Do sports fans care about climate change? Can sports communication be used to engage audiences on environmental sustainability? In this section of the course, students will use the tools of data science to examine whether interest in sports is associated with climate change knowledge, attitudes and behaviors, as well as other political opinions. Participants will use survey data to produce visualizations and exploratory analyses about the relationship between sports fandom and attitudes about environmental sustainability.

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, Linguistics, Political Science, or Writing & Rhetoric. This course will use the R programming language. No prior experience with R is necessary.   GEOG: Students will apply data science tools to explore the geography human-environment relationships around protected areas. We will use household survey and land cover data from locations across the humid tropics where the Wildlife Conservation Society has been tracking human wellbeing and forest resource use in high-priority conservation landscapes. Projects and visualizations will be presented back to WCS to inform their ongoing monitoring and management in these sites.   LNGT: In this section, we will learn how to collect and analyze Twitter data in R. We will focus on social metrics and geographical locations to examine language variation in online communities across the United States. While the emphasis will be placed on linguistics, the statistical and analytical tools will help you work with other types of Twitter corpora in the future.   PSCI: Students will use cross-national data to explore relationships between conflict events and political, social, and economic factors in each nation. What factors contribute to conflict and violence? Our focus will be to find patterns in the data using the tools in R and discuss what those patterns suggest for addressing rising conflict and resolving ones that have already experienced violence.   WRPR: Students will learn to conduct writing studies research through working with "big data” from a multiyear survey of first-year college students about their academic confidences, attitudes, and perceptions. We will explore how educational access, identity, and language background impacts survey responses. Using statistical analysis and data visualizations, as well as writing, we will report our findings.

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.  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.     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.  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.   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.

Ecology and Evolution In this introduction to ecology and evolutionary biology we will cover the topics of interspecific interactions (competition, predation, mutualism), demography and life-history patterns, succession and disturbance in natural communities, species diversity, stability and complexity, causes of evolutionary change, speciation, phylogenetic reconstruction, and population genetics.   The laboratory component will examine lecture topics in detail (such as measuring the evolutionary response of bacteria, adaptations of stream invertebrates to life in moving water, invasive species and their patterns of spread).  We will emphasize experimental design, data collection in the field and in the laboratory, data analysis, and writing skills. This course is not open to seniors and second semester juniors in the Fall.

David Allen

Reviews and ratings for David Allen at Middlebury College.David Allen teaches Plant Community Ecology, Biostatistics, Biostatistics II and more.  Explore top rated instructors and find the best for you.Is David Allen good ? Find out on MiddCourses.

Dave Allen is clearly so passionate about the subject and that is what makes him such a good teacher. I really enjoyed the labs for this class as they were mostly walking in the woods and identify the plants/ and organisms we saw. Highly recommend if you want to learn more about Vermont Forests!

Laid back classes with lots of time outdoors and interesting content. The lecture material followed a nice progression. We also only had 1-2 major assignments that we thoroughly worked on throughout the semester. Fun class. 

I would recommend taking this class with Professor Allen, as opposed to other Bio professors. He's an easy grader, the labs never take that long, and he is a super nice guy. If you interested in learning how to use R studio, this course is for you. If you interested in the more fundamental statistics behind the computer program, take a Stats class in the Math department. 