This course provides students in the social and natural sciences with the skills necessary to perform elementary statistical analysis. Topics include descriptive measures, sampling theory, Student-T and normal distributions, estimation and hypothesis testing with p-values, regression and correlation. This course may not be used for the Mathematics major or minor. Contributes to Data Analytics, Data Science, and Health Studies. (Fall, Spring) (NS)

This course is a calculus-based, mathematical introduction to the fundamental principles of probability theory and applications. Topics include combinatorial analysis used in computing probabilities, the axioms and properties of probability, conditional probability, independence of events, discrete and continuous random variables, the standard distributions, expected value and variance, joint distributions, distributions of a function of a random variable, and sampling distributions. Also included are theoretical results such as Bayes' Theorem, Central Limit Theorem, Law of Large Numbers, the Empirical Rule, Hypothesis Testing and Confidence intervals at least for a single mean and a single proportion. Contributes to Data Analytics and Data Science. Prerequisite: Mathematics 52-264. (Spring) (NS)

This course is an introduction to the discipline of computer science with an emphasis on applications in the liberal arts. Topics include basic programming constructs, basic data structures, algorithmic computation, selection, iteration, interactive user interfaces, abstraction and reasoning about computer programs. This is an introductory course intended for humanities, social science and fine arts majors. May not be used for the Computer Science major or minor. Cannot be taken after successful completion of Computer Science 54-184, 54-284, or 54-454 without departmental approval. Contributes to Data Analytics and Data Science. (NS)

This is the standard first course in computer programming in an object-oriented style. It is primarily intended for students pursuing a major or minor in computer science, mathematics or other disciplines in the natural sciences. Topics include primitive types and operations, assignment, conditional execution, iteration, arrays, classes, methods, recursion, encapsulation, type extension, inheritance and reasoning about programs in Java. The course includes a laboratory component designed to explore applications and to enhance conceptualization. Contributes to Data Analytics and Data Science. (Fall, Spring) (NS)

This course aims to cultivate strong exploratory data analysis skills to get meaningful insight into data for decision making and research problem solving for students in social and natural sciences. The main focus of this course is to provide hands-on experience in discovering and analyzing patterns, trends, correlations and associations between variables in data using numerical and graphical data analysis tools to make data-driven decisions. This course also focuses on hypothesis testing and population parameter estimation to enhance students' ability in conducting scientific research that aims to solve real-life problems. Topics include: programming in R, deep exploratory data analysis, introduction to probability, hypothesis testing (using Student-T test, proportion test, chi-square test, and analysis of variance), multiple linear regression and logistic regression. Prerequisite: Mathematics 52-114 and sophomore standing, or permission of instructor. (Fall)

This lecture/laboratory course is a foundation-building course that contains instruction on reading the primary literature in ecology and evolutionary biology, conducting literature searches, designing experiments, writing scientific papers, using quantitative methods, exercising critical thinking skills for data analyses, creating graphs, and developing specific laboratory and field research skills for ecology and evolutionary biology. Contributes to Data Analytics and Data Science .Prerequisite: Biology 50-123/121 and 50-133/131, or Biology 50-173/171 and 50-183/181, and Mathematics 52-114. (Fall and Spring) (NSL) (WA)

This course introduces students to analytical chemistry through participation in a semester-long research project. Students work in groups to develop an original research question and create a proposed research plan, then carry out analytical experiments using available spectroscopic, chromatographic, and mass spectral instrumentation. The course also focuses on conducting appropriate statistical analyses of collected data and contextualizing experimental results within the broader chemical literature. Contributes to Data Analytics and Data Science. Prerequisites: Chemistry 51-201 and Chemistry 51-321. (NS) (WA)

An exploration of human phenotype, including Mendelian, polygenic and environmental influences, using approaches ranging from family studies and evolutionary medicine to population genomics. The lab will focus on bioinformatic resources in genetics, genomics, and medicine. Contributes to Data Analytics and Data Science. Prerequisite: Biology 50-123/121 and 50-133/131, or Biology 50-173/171 and 50-183/181, and one of the following: Biology 50-222, Kinesiology 48-214, or Psychology 33-204, 33-214, or 33-224. (NSL)

Formulation and solution of problems with management, economics, engineering and science applications using modeling, optimization techniques, and simulation. Topics include linear and integer programming, simplex method, duality, sensitivity analysis, branch and bound algorithm, transportation and assignment problems, network optimization, and problem solving using optimization software. Also Mathematics 52-414 and Computer Science 54-414. Contributes to Data Analytics and Data Science. Prerequisites: Mathematics 52-164 (Modern Calculus I), and either Business 30-474 (Finance), Mathematics 52-674 (Linear Algebra), some Computer Science course at the 300 level or above, or permission of the instructor.

Formulation and solution of problems with management, economics, engineering and science applications using modeling, optimization techniques, and simulation. Topics include linear and integer programming, simplex method, duality, sensitivity analysis, branch and bound algorithm, transportation and assignment problems, network optimization, and problem solving using optimization software. Also Mathematics 52-414 and Business 30-414. Contributes to Data Analytics and Data Science. Prerequisites: Mathematics 52-164 (Modern Calculus I), and either Business 30-474 (Finance), Mathematics 52-674 (Linear Algebra), some Computer Science course at the 300 level or above, or permission of the instructor.

An introduction to the theory and practice of database management systems. Topics include database terminology, the entity-relationship model, the relational model, normalization, querying databases using SQL, and exploration of other database technologies. The course includes a laboratory component designed to explore applications and to enhance conceptualization. Contributes to Data Analytics and Data Science. Prerequisite: Computer Science 54-284 or permission of instructor. (NS)

Quantitative and qualitative research methods for economic problems. Research design, data collection and statistical analysis of cross-sectional data are covered. A major research paper and a regular computer lab are required. This course is intended for Economics majors. Prerequisites: Economics 31-224, 31-234 and Mathematics 52-114, or permission of instructor. Contributes to Data Analytics and Data Science. (Fall) (WA) (ScS)

This course introduces students to the practice and theory of Geographic Information Systems (GIS) as a method for analysis of the environment. Students will examine the fundamentals of GIS and GIS applications, learning the concepts needed to effectively manipulate, query, analyze, and visualize spatial-based data. At the end of the semester students should feel comfortable applying GIS to a range of environmental issues, and have a solid understanding of the procedures and data necessary to conduct geographical analysis. Contributes to Data Analytics and Data Science. (NSL)

This course surveys the history of Europe from the late eighteenth century revolutions through the creation and expansion of the European Union. We explore social, political, intellectual, and cultural developments, paying particular attention to reform movements and revolutions. The course tracks shifting ideas of gender, race, and class, as well as examining Europe's role in the world. Contributes to Data Analytics, Data Science, Feminist Studies, and International Studies. (Biennially) (H)

This course covers the basic concepts of research methods used in the discipline of kinesiology. It is designed to help students think critically, to give students hands-on experiences with research design, data analysis and interpretation, and to report results to a professional audience. Contributes to Data Analytics, Data Science, and Exercise and Sport Studies. Prerequisite: Mathematics 52-114 or permission of instructor. (Fall and Spring) (NS) (WA)

See Computer Science 54-414 and Business 30-414. Contributes to Data Analytics and Data Science.

This course focuses on the emergence of structural properties from atomic and molecular-scale interactions by conducting a survey of three broad classes of materials: metals, ceramics and polymers. Particular attention will be paid to atomic structure and bonding, the structure of crystalline solids, phase diagrams, and the application and processing of polymers deployed in additive manufacturing (e.g., 3D Printing) and biomaterials. Also Chemistry 51-424. Contributes to Data Analytics and Data Science. Prerequisites: Physics 53-154. Chemistry 51-103 is a pre- or co-requisite for this course. (NS) (Spring)

This course will introduce students to basic approaches to research design and analysis in political science. Over the course of the semester, we will design research topics and questions, develop empirically testable hypotheses, collect relevant data, and apply basic qualitative and quantitative data analysis techniques. Topics covered in this course will span all areas of politics, international relations, and political institutions. Pre-requisites: Political Science 32-114 or 32-144 or permission of instructor. Contributes to Data Analytics, Data Science and Design Thinking. (ScS)

This course analyzes political issues from a psychological perspective to assess the role that the political brain plays in shaping our institutions, public policy, and political behavior. The course also introduces students to research methods typical in the study of political behavior. Contributes to Data Analytics, Data Science, and Psychology. Pre-requisites: Political Science 32-114 or Psychology 33-104. (ScS)

This course explores the factors that shape public opinion, from question wording to socialization to the media and beyond. How do people arrive at their political opinions and how can we be sure that these opinions are grounded in facts and rational understanding of the issues at hand? We investigate the malleability of public opinion and under what conditions government officials should (and do) take it under consideration when making political decisions. We also conduct our own public opinion research, paying attention to the methods used to assess individual opinions and how these methods shape our understanding of what the public wants. Contributes to Data Analytics and Data Science. Prerequisites: Political Science 32-364 or 32-384, or permission of instructor. (ScS) (American Politics)

This course introduces students to a variety of research methods in psychology, including experimental and non-experimental designs. Topics include literature review, hypothesis formation, psychological measurement, sampling, design, statistical analysis, ethics, and scientific writing in APA style. This course (with no lab) covers the basic methodological background necessary for upper-level psychology courses but does not involve project-based research. It is recommended for (a) psychology majors pursuing non-psychology careers or who prefer an internship capstone rather than a research capstone, (b) psychology minors, and (c) non-psychology majors preparing to take the MCAT. Contributes to Data Analytics and Data Science. Prerequisites: Psychology 33-104 and Mathematics 52-114, minimum grades of C required. (Spring) (WA) (ScS)

This course gives students in-depth experience with the research methods used in psychology, including experimental and non-experimental designs. Topics include literature review, hypothesis formation, psychological measurement, sampling, statistical analysis, ethics, and scientific writing in APA style. This course (which includes a required 1-credit lab; 33-211) involves intense project-based original research, and serves as good preparation for later independent research (capstone research or graduate school). This course is recommended for (a) psychology majors who prefer a research capstone over an internship capstone, and (b) students considering applying to graduate school in psychology. Co-requisite: Concurrent registration in 33-211, with the same professor, is required. You must register for lecture and lab with matching section numbers (e.g. 33-214-01 & 33-211-01). Contributes to Data Analytics and Data Science. Prerequisites: Psychology 33-104 and Mathematics 52-114, minimum grades of C required. (Fall, Spring) (WA) (ScS) Not appropriate for first-year students.