Academic Catalog 2018-2019 [ARCHIVED CATALOG]
Course Listing
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Courses listed below are grouped together alphabetically by subject prefix. To search for a specific course, please follow the instructions in the course filter box below and click on “Filter.”
Departments and interdisciplinary programs are described in detail on the Majors, Minors, and Other Programs page within this catalog. Please refer to the detailed sections on each area of study for more information. Requirements to fulfill a major or minor appear within each program or area of study.
All students must also complete the courses in the Common Curriculum (General Education), including Writing Across the Curriculum (WAC) requirements and other requirements that pertain to the undergraduate degree. Courses are numbered as follows.
000-049 - Non-credit courses.
050-099 - Common Curriculum (General Education) courses and others that do NOT count toward the major.
100-199 - Introductory-level courses which count for the major.
200-299 - Sophomore/junior-level courses that can be taken by non-majors. (Some departments may use 200-249 and 250-259 to delineate between sophomore and junior level offerings.)
300-399 - Upper-level courses intended primarily for majors - these are courses representing the depth component of the major.
400-499 - All advanced courses for seniors, including those used to fulfill WS (Senior Writing Experience requirement), small seminars, research, thesis, and independent studies.
Wherever possible, the departments have indicated the instructor and the term during which a course is given. Some courses are offered only occasionally and are so indicated. The College retains the right not to offer a course, especially if enrollment is insufficient.
A few courses are not valued at full course credit, and some carry double credit.
A full course unit may be equated to five quarter-credit hours, or three and one-third semester credit hours.
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Spanish |
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SPN 492 - Spanish Independent Study 3 Course Units: 1
(Fall, Winter, Spring; Staff) Individual directed readings in the field of Spanish or Spanish-American literature. Prerequisite(s): At least one course in Spanish at the 400-level and permission of the instructor.
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Statistics |
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STA 064 - (MTH-064) Statistical Thinking Course Units: 1
(Not Offered this Academic Year) Seeks to provide the conceptual foundation and analytical skills required to understand a complex, data-rich and uncertain world, and to navigate through the daily bombardment of data from all sides. Significant emphasis is given to understanding the difficulties in acquiring high-quality data, before moving on to graphical and statistical analysis of data, in order to draw actionable conclusions. CC: QMR Note: Not open to students who have passed STA 104 , ECO 243 , MER 301 , PSY 200 or a college calculus course.
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STA 104 - (MTH-104) Introduction to Statistics Course Units: 1
(Fall, Winter; Staff) This course is intended to provide the conceptual foundations, and also analytical skills, for students to be able to quantify uncertainty, and further, to make rational decisions in the face of uncertainty. It addresses collection of high-quality data, basic statistical analysis of such data, including use of computer software, and drawing actionable conclusions from analyses. These conclusions include understanding the limitations of statistical analyses. The integration of subject matter knowledge with data analysis within the sequential cycle of scientific inquiry will be emphasized. This course is also intended to prepare students for more advanced statistics courses, such as those in experimental design or regression analysis. CC: QMR Note: This course is designed for first year students and sophomores, and preference will be given to such students in accepting petitions. More advanced statistics courses, such as STA 164 (MTH 164) or STA 264 (MTH 264) , may be more appropriate for upper class students with solid backgrounds in calculus, e.g., who have taken MTH 112 . Not open to students who have passed STA 064 , STA 164 , STA 264 , MER 301 , ECO 243 , or PSY 200 .
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STA 128 - Probability Course Units: 1
(Winter; Staff) (Same as MTH 128 ) Probability theory and applications. Prerequisite(s): MTH 102 , MTH 112 , or MTH 113
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STA 164 - (MTH 164) Strategies of Experimentation: Statistical Design and Analysis of Experiments Course Units: 1
(Spring; Staff) Experimentation is at the heart of the scientific method, both in the physical and social sciences. Not only do experiments validate or disprove existing hypotheses, but often unexpected results lead to the development of new hypotheses and new theoretical understanding. This course will focus on strategies to accelerate the scientific method when experimenting with multiple variables. Specific topics include design options, such as simple comparative experiments, factorials and fractional factorials, and response surface designs, as well as analysis methods such as graphical methods, analysis of variance, and regression models. Prerequisite(s): STA 104 (MTH 104), STA 128 (MTH 128 ), STA 264 (MTH 264) , MER 301 , ECO 243 , PSY 200 or permission from the Chair, which may be granted to upper class students who have solid backgrounds in calculus, e.g., who have taken MTH 112 .
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STA 264 - (MTH 264) Regression Analysis Course Units: 1
(Winter; Staff) Regression analysis is one of the most important and influential methods in statistics, finding application in virtually all disciplines, from business to healthcare to sociology to the hard sciences. This course will cover both the science of regression analysis - its underlying mathematical theory, as well as the art of its practical application. The course project will involve development of a regression model to fit a real data set. Lectures will be given primarily in matrix notation, i.e., using linear algebra. While the course will not be all-encompassing in itself due to time constraints, it would be good preparation for more advanced modeling courses involving data mining, machine learning, “Big Data”, and so on. Prior understanding of statistical concepts is assumed Prerequisite(s): MTH 115 and STA 104 (MTH-104) , ECO 243 , STA 164 (MTH 164) , PSY 200 , MER 301 or permission from the Chair, which may be granted to upper class students who have solid backgrounds in calculus and linear algebra, e.g., who have taken MTH 115 .
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STA 295H - Statistics Honors Independent Project 1 Course Units: 0
(Staff)
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STA 296H - Statistics Honors Independent Project 2 Course Units: 1
(Staff)
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STA 364 - Big Data Analytics Course Units: 1
(Spring; Staff) This course focuses on the analysis of large data sets in diverse application areas using statistical programming languages. Students will develop an understanding of the role of machine learning methods within the context of the scientific method. They will analyze real data sets using downloadable statistical programming packages, including on a course project of their own choosing. This analysis will include exploratory data analysis, visualization, and use of more sophisticated classification and predictive algorithms including nearest neighbor, naïve Bayes, classification and regression trees (CART), neural networks, and others. During the course we will pay special attention to validating models using the “train and test” regimen, as well as through cross validation and bootstrapping. In the process of studying the machine learning methods themselves, students will develop an ability to manipulate big data to accomplish the previous objectives. This includes downloading, merging, appending and reshaping data, and creating new variables. Successful completion of this course would be advantageous for those considering graduate study or employment in the areas of statistics, data science, machine learning, computer science, econometrics, or related disciplines. Prerequisite(s): STA 264 or ECO 243 or permission from the Chair.
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STA 490 - Statistics Independent Study Course Units: 1
(Fall, Winter, Spring; Staff) Independent study in a particular area of statistics under the supervision of a faculty member. Note: Subject to faculty availability and Chair approval.
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