Exponents, Logarithms, and Their Applications (Half Credit) Students will explore the fundamental concepts of exponents and logarithms and gain proficiency in algebraic manipulation of these functions. We will explore their wide-ranging applications across various fields of mathematics and real-world scenarios, including compound interest, population growth, radioactive decay and carbon dating, and the pH scale. We will discuss strategies for how to be successful as a student in a college-level math class, how to work together to solve problems, and how to harness students’ strengths for maximum efficiency in learning. Emphasis is placed on developing problem-solving skills and mathematical reasoning in preparation for calculus and related fields. (Students are encouraged, but not required, to enroll in MATH 0103 in the same semester)

Functions Students will explore various topics essential for success in calculus. These include solving equations and inequalities, functions and their transformations, polynomial and rational functions, exponential and logarithmic functions, and trigonometric functions and their applications. Students will develop proficiency in algebraic manipulation and graphing skills to visualize functions and analyze their characteristics. We will discuss strategies for how to be successful as a student in a college-level math class, how to work together to solve problems, and how to harness students’ personal strengths for maximum efficiency in learning. Emphasis is placed on developing problem-solving skills and mathematical reasoning in preparation for calculus and related fields. (Students are encouraged, but not required, to enroll in MATH 102 in the same semester)

Exponents, Logarithms, Functions Students will explore various topics essential for success in calculus. Students will explore the fundamental concepts of exponents and logarithms and explore their wide-ranging applications across various fields of mathematics and real-world scenarios, including compound interest, population growth, radioactive decay and carbon dating, and the pH scale. Solving equations and inequalities, functions and their transformations, polynomial and rational functions, and trigonometric functions and their applications will also be discussed. Students will develop proficiency in algebraic manipulation and graphing skills to visualize functions and analyze their characteristics. We will discuss strategies for how to be successful as a student in a college-level math class, how to work together to solve problems, and how to harness students’ personal strengths for maximum efficiency in learning. Emphasis is placed on developing problem-solving skills and mathematical reasoning in preparation for calculus and related fields. (Equivalent to MATH 102 + MATH 103)

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. (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.

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 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 Exps/Logs and Applications, Functions, Exponents Logarithms Functions and more.  Explore top rated instructors and find the best for you.Is Emily Malcolm-White good ? Find out on MiddCourses.

EMW is such a nice person, and she'll genuinely be so nice about extensions and all that stuff. I think the way she teaches the R-concepts is way more just her doing it and you just formatting it in the same way, so it's not too bad doing the homework after class(btw her hw is the cw).

Professor Emily is my personal favorite professors at Middlebury. Her data science class was clearly structured with guided R notes and in class work time for both classwork and homework that helped keep the workload very manageable. She explains material in an approachable way and is always available for help, both in class and during office hours. The course is heavily weighted on two major projects. Overall, she makes learning R enjoyable and I would highly recommend.

Very project heavy. Sometimes the reports due date would overlap but enough time was given for each report. Classroom environment was a bit intimidating to ask questions, but prof is always available for office hours. This course was also more coding than I was prepared for so a lot of my studying wasn't necessarily for statistics but to understand how to code.