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.

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.

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.

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

I believe Dave Allen was teaching 3 sections a day and for this reason I understand his slightly lackluster teaching style. However, he was fair, kind, and his overall affect was pleasent. I was able to succeed in this class with minimal effort and while my interset in the subject was not high there was nothing about this class that should steer someone away from taking this pre-requisite with Dave Allen

The class was broken into two pieces. The first half of the day was with David Allen and the Second Half with professor L'Roe. The limited amount of time in J term forced the teaching and lectures to be rushed. Furthermore, the afternoon professor was new to R making the teaching not as helpful as I would've hoped. I recommend taking data science during a regular semester instead of during J-term.

I really enjoyed this course and it was not super hard. There were two open note exams and one major lab report. Dave is a nice and chill professor and grades fairly. A lot of the labs were outside which was nice.