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.

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.  (MATH 0200; and MATH 0116 or STAT 0116, or MATH 0311 or STAT 0311) 3 hrs lect./disc.

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. (Credit is not given for MATH 0116 if the student has taken ECON 0111 (formerly ECON 0210) or PSYC 0201 previously or concurrently.)

Introduction to Statistical Science 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. (Credit is not given for MATH 0116 if the student has taken ECON 0111 (formerly ECON 0210) or PSYC 0201 previously or concurrently.)

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.

Calculus I Introductory analytic geometry and calculus. Topics include limits, continuity, differential calculus of algebraic and trigonometric functions with applications to curve sketching, optimization problems and related rates, the indefinite and definite integral, area under a curve, and the fundamental theorem of calculus. Inverse functions and the logarithmic and exponential functions are also introduced along with applications to exponential growth and decay.

Emily Malcolm-White

Reviews and ratings for Emily Malcolm-White at Middlebury College.Emily Malcolm-White teaches Introduction to Data Science, Regression, Intro to Statistical Science and more.  Explore top rated instructors and find the best for you.Is Emily Malcolm-White good ? Find out on MiddCourses.

This class was lecture-based and then you would complete the lessons/homework in class to ensure you understood the content. The lectures were important to understanding the homeworks, which was 50% of the grade. I really recommend Professor Malcolm-White, she's super enthusiastic and answers questions extremely well.   

This class was an easy introduction to statistics. It was not very fast paced and the lectures were easy to follow. Lots of the homework was simple and the exams were open note. Definitely recommend it.

For a person who never wants to have anything more to do with maths beyond high school, this course was surprisingly pleasant to take. It wasn't dead-easy but also wasn't insufferably challenging. Prof Emily is very chill and friendly, she's good at explaining stuff in simple language and is always helpful during class and office hours. Classtime is basically going over stats knowledge and starting on homework, labs are dedicated to learning R and starting some more homework. Workload isn't heavy and there are only two (OPEN-BOOK, OPEN-INTERNET!!) exams for the semester. Great experience.