{"id":27,"date":"2018-01-17T06:29:12","date_gmt":"2018-01-17T06:29:12","guid":{"rendered":"https:\/\/www.macalester.edu\/160-cognitive-science\/schedules\/"},"modified":"2024-05-23T11:34:22","modified_gmt":"2024-05-23T16:34:22","slug":"schedules","status":"publish","type":"page","link":"https:\/\/www.macalester.edu\/cognitive-science\/schedules\/","title":{"rendered":"Class Schedules"},"content":{"rendered":"
\n <\/a>\n

\n \n Fall 2026<\/a>\n \n \n Spring 2027<\/a>\n \n \n\t\n\t\n\t\n <\/p>\n \n\t

Fall 2026<\/h2>\n

Visit the Registrar's Class Schedule for live registration information<\/a><\/p>\n

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Num. \/ Sec. \/ CRN<\/th>\n Name<\/th>\n Days<\/th>\n Time<\/th>\n Room<\/th>\n Instructor<\/th>\n <\/th>\n \n <\/tr>\n <\/thead>\n
LING 100-01 10451<\/span><\/td>\n Introduction to Linguistics<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span>HUM 400\n <\/td>\n \n Instructor: <\/span>Morgan Sleeper\n <\/td>\n \n \n <\/td>\n <\/tr>\n
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<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n The aim of this course is to make you aware of the complex organization and systematic nature of language. You will be studying yourself and those around you, since you are all language users. Most of your knowledge of language, however, is unconscious, and earlier education may have given you confusing or misleading notions about language. This course is intended to clarify your ideas about language and bring you to a better understanding of its nature. You will learn more about how languages work by observing and analyzing\u00a0 language(s) (from people you know, from yourself, videos, in-class data sets, social media, texting etc.)\u00a0 By the end of the course you should be familiar with some of the terminology and techniques of linguistic analysis and be able to apply this knowledge to the description of different languages.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

PSYC 100-01 10596<\/span><\/td>\n Introduction to Psychology<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>01:10 pm-02:10 pm\n <\/td>\n \n Room: <\/span>THEATR 202\n <\/td>\n \n Instructor: <\/span>Cari Gillen-O'Neel\n <\/td>\n \n \n <\/td>\n <\/tr>\n
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\n \n Details\n <\/a>\n
\n

\n An introduction to psychological science -- the study of behavior and mental processes. This course surveys the major subdisciplines of the field, including such topics as the brain and neuroscience, behavioral genetics, cognitive and social development, perception, learning, memory, decision-making, language, consciousness, emotions, motivation, psychological disorders, social identity, interpersonal interactions and group and\u00a0cultural processes. Lecture and laboratory components.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Writing WP\n \n \n Quantitative Thinking Q1\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

PSYC 100-02 10597<\/span><\/td>\n Introduction to Psychology<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>02:20 pm-03:20 pm\n <\/td>\n \n Room: <\/span>THEATR 202\n <\/td>\n \n Instructor: <\/span>Cari Gillen-O'Neel\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n An introduction to psychological science -- the study of behavior and mental processes. This course surveys the major subdisciplines of the field, including such topics as the brain and neuroscience, behavioral genetics, cognitive and social development, perception, learning, memory, decision-making, language, consciousness, emotions, motivation, psychological disorders, social identity, interpersonal interactions and group and\u00a0cultural processes. Lecture and laboratory components.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Writing WP\n \n \n Quantitative Thinking Q1\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

COMP 112-01 10183<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span>THEATR 203\n <\/td>\n \n Instructor: <\/span>Amin Alhashim\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with STAT 112-01 (10184); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 112-01 10184<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span>THEATR 203\n <\/td>\n \n Instructor: <\/span>Amin Alhashim\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 112-01 (10183); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

COMP 112-02 10185<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>10:50 am-11:50 am\n <\/td>\n \n Room: <\/span>THEATR 203\n <\/td>\n \n Instructor: <\/span>Amin Alhashim\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with STAT 112-02 (10186); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 112-02 10186<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>10:50 am-11:50 am\n <\/td>\n \n Room: <\/span>THEATR 203\n <\/td>\n \n Instructor: <\/span>Amin Alhashim\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 112-02 (10185); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

COMP 112-03 10187<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>12:00 pm-01:00 pm\n <\/td>\n \n Room: <\/span>OLRI 241\n <\/td>\n \n Instructor: <\/span>Getiria Onsongo\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with STAT 112-03 (10188); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 112-03 10188<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>12:00 pm-01:00 pm\n <\/td>\n \n Room: <\/span>OLRI 241\n <\/td>\n \n Instructor: <\/span>Getiria Onsongo\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 112-03 (10187); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

COMP 112-04 10189<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>03:30 pm-04:30 pm\n <\/td>\n \n Room: <\/span>OLRI 241\n <\/td>\n \n Instructor: <\/span>Getiria Onsongo\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with STAT 112-04 (10190); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 112-04 10190<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>03:30 pm-04:30 pm\n <\/td>\n \n Room: <\/span>OLRI 241\n <\/td>\n \n Instructor: <\/span>Getiria Onsongo\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 112-04 (10189); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

ECON 119-01 10216<\/span><\/td>\n Principles of Economics<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span>CARN 206\n <\/td>\n \n Instructor: <\/span>Jessica Kiser\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n Economics is the study of how people make decisions and how these decisions apply to real-world problems. Economics can help us understand income inequality within and across countries, the quality of the environment, unemployment, poverty, crime, health care, financial crises, technological change, inflation and many more issues. This course introduces the basic tools that economists use to explore these topics and will cover fundamental economic concepts like scarcity, supply and demand, costs and benefits, trade-offs, and incentives. This course is a one semester overview of both microeconomics (the study of choices firms and individual consumers make) and macroeconomics (the study of the economy as a whole).\u00a0This course counts as a Group E economics course and is the prerequisite for 200-level E courses in the Economics Department.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

ECON 119-02 10217<\/span><\/td>\n Principles of Economics<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>10:50 am-11:50 am\n <\/td>\n \n Room: <\/span>CARN 206\n <\/td>\n \n Instructor: <\/span>Jessica Kiser\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n Economics is the study of how people make decisions and how these decisions apply to real-world problems. Economics can help us understand income inequality within and across countries, the quality of the environment, unemployment, poverty, crime, health care, financial crises, technological change, inflation and many more issues. This course introduces the basic tools that economists use to explore these topics and will cover fundamental economic concepts like scarcity, supply and demand, costs and benefits, trade-offs, and incentives. This course is a one semester overview of both microeconomics (the study of choices firms and individual consumers make) and macroeconomics (the study of the economy as a whole).\u00a0This course counts as a Group E economics course and is the prerequisite for 200-level E courses in the Economics Department.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

ECON 119-03 10218<\/span><\/td>\n Principles of Economics<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>10:50 am-11:50 am\n <\/td>\n \n Room: <\/span>CARN 107\n <\/td>\n \n Instructor: <\/span>Ioannis Koutsonikolis\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n Economics is the study of how people make decisions and how these decisions apply to real-world problems. Economics can help us understand income inequality within and across countries, the quality of the environment, unemployment, poverty, crime, health care, financial crises, technological change, inflation and many more issues. This course introduces the basic tools that economists use to explore these topics and will cover fundamental economic concepts like scarcity, supply and demand, costs and benefits, trade-offs, and incentives. This course is a one semester overview of both microeconomics (the study of choices firms and individual consumers make) and macroeconomics (the study of the economy as a whole).\u00a0This course counts as a Group E economics course and is the prerequisite for 200-level E courses in the Economics Department.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

ECON 119-04 10219<\/span><\/td>\n Principles of Economics<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>01:20 pm-02:50 pm\n <\/td>\n \n Room: <\/span>CARN 305\n <\/td>\n \n Instructor: <\/span>Sarah West\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n Economics is the study of how people make decisions and how these decisions apply to real-world problems. Economics can help us understand income inequality within and across countries, the quality of the environment, unemployment, poverty, crime, health care, financial crises, technological change, inflation and many more issues. This course introduces the basic tools that economists use to explore these topics and will cover fundamental economic concepts like scarcity, supply and demand, costs and benefits, trade-offs, and incentives. This course is a one semester overview of both microeconomics (the study of choices firms and individual consumers make) and macroeconomics (the study of the economy as a whole).\u00a0This course counts as a Group E economics course and is the prerequisite for 200-level E courses in the Economics Department.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

ECON 119-05 10220<\/span><\/td>\n Principles of Economics<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>02:20 pm-03:20 pm\n <\/td>\n \n Room: <\/span>CARN 305\n <\/td>\n \n Instructor: <\/span>Alexander Von Hafften\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n Economics is the study of how people make decisions and how these decisions apply to real-world problems. Economics can help us understand income inequality within and across countries, the quality of the environment, unemployment, poverty, crime, health care, financial crises, technological change, inflation and many more issues. This course introduces the basic tools that economists use to explore these topics and will cover fundamental economic concepts like scarcity, supply and demand, costs and benefits, trade-offs, and incentives. This course is a one semester overview of both microeconomics (the study of choices firms and individual consumers make) and macroeconomics (the study of the economy as a whole).\u00a0This course counts as a Group E economics course and is the prerequisite for 200-level E courses in the Economics Department.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

COMP 123-01 10191<\/span><\/td>\n Core Concepts in Computer Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>10:50 am-11:50 am\n <\/td>\n \n Room: <\/span>OLRI 258\n <\/td>\n \n Instructor: <\/span>Elizabeth Ernst\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course introduces the field of computer science, including central concepts such as the design and implementation of algorithms and programs, testing and analyzing programs, the representation of information within the computer, and the role of abstraction and metaphor in computer science. The exploration of these central ideas will draw examples from a range of application areas including multimedia processing, turtle graphics, and text processing. Course work will use the Python programming language.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

COMP 123-02 10192<\/span><\/td>\n Core Concepts in Computer Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>12:00 pm-01:00 pm\n <\/td>\n \n Room: <\/span>OLRI 258\n <\/td>\n \n Instructor: <\/span>Elizabeth Ernst\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course introduces the field of computer science, including central concepts such as the design and implementation of algorithms and programs, testing and analyzing programs, the representation of information within the computer, and the role of abstraction and metaphor in computer science. The exploration of these central ideas will draw examples from a range of application areas including multimedia processing, turtle graphics, and text processing. Course work will use the Python programming language.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-01 10706<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span>OLRI 241\n <\/td>\n \n Instructor: <\/span>Brianna Heggeseth\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, data visualizations, multivariate linear regression, logistic regression, probability, model building and interpretation (i.e., confounding variables, causal diagrams, data context), and statistical inference (i.e., confidence intervals and hypothesis testing).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-02 10707<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>08:00 am-09:30 am\n <\/td>\n \n Room: <\/span>THEATR 205\n <\/td>\n \n Instructor: <\/span>Lori Ziegelmeier\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, data visualizations, multivariate linear regression, logistic regression, probability, model building and interpretation (i.e., confounding variables, causal diagrams, data context), and statistical inference (i.e., confidence intervals and hypothesis testing).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-03 10708<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>01:10 pm-02:10 pm\n <\/td>\n \n Room: <\/span>OLRI 241\n <\/td>\n \n Instructor: <\/span>Abby Loe\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, data visualizations, multivariate linear regression, logistic regression, probability, model building and interpretation (i.e., confounding variables, causal diagrams, data context), and statistical inference (i.e., confidence intervals and hypothesis testing).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-04 10709<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>02:20 pm-03:20 pm\n <\/td>\n \n Room: <\/span>OLRI 241\n <\/td>\n \n Instructor: <\/span>Abby Loe\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, data visualizations, multivariate linear regression, logistic regression, probability, model building and interpretation (i.e., confounding variables, causal diagrams, data context), and statistical inference (i.e., confidence intervals and hypothesis testing).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-05 10710<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>09:40 am-11:10 am\n <\/td>\n \n Room: <\/span>THEATR 205\n <\/td>\n \n Instructor: <\/span>Lori Ziegelmeier\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, data visualizations, multivariate linear regression, logistic regression, probability, model building and interpretation (i.e., confounding variables, causal diagrams, data context), and statistical inference (i.e., confidence intervals and hypothesis testing).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-06 10711<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>01:20 pm-02:50 pm\n <\/td>\n \n Room: <\/span>THEATR 205\n <\/td>\n \n Instructor: <\/span>Lori Ziegelmeier\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, data visualizations, multivariate linear regression, logistic regression, probability, model building and interpretation (i.e., confounding variables, causal diagrams, data context), and statistical inference (i.e., confidence intervals and hypothesis testing).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

PSYC 201-01 10603<\/span><\/td>\n Research in Psychology I<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span>OLRI 352\n <\/td>\n \n Instructor: <\/span>Steve Guglielmo\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Students registered for this section must register for either PSYC 201-L1 or L2*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course is an introduction to the basic principles of research in psychology, with an emphasis on statistical techniques used in psychological science.\u00a0We examine how to test psychological hypotheses using various statistical analyses, and we consider the pros and cons of experimental, quasi-experimental, and correlational research designs.\u00a0The course includes a weekly laboratory component in which students develop proficiency with statistical software, writing reports in American Psychological Association style, and familiarity with experimental techniques unique to behavioral research. Prerequisite(s): PSYC 100\u00a0Permission of instructor is required for first year students.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

PSYC 201-02 10604<\/span><\/td>\n Research in Psychology I<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>08:30 am-09:30 am\n <\/td>\n \n Room: <\/span>OLRI 352\n <\/td>\n \n Instructor: <\/span>Ariel James\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Students registered for this section must register for either PSYC 201-L3 or L4*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course is an introduction to the basic principles of research in psychology, with an emphasis on statistical techniques used in psychological science.\u00a0We examine how to test psychological hypotheses using various statistical analyses, and we consider the pros and cons of experimental, quasi-experimental, and correlational research designs.\u00a0The course includes a weekly laboratory component in which students develop proficiency with statistical software, writing reports in American Psychological Association style, and familiarity with experimental techniques unique to behavioral research. Prerequisite(s): PSYC 100\u00a0Permission of instructor is required for first year students.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

PHIL 213-01 10537<\/span><\/td>\n Philosophy of Mind<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>01:20 pm-02:50 pm\n <\/td>\n \n Room: <\/span>HUM 215\n <\/td>\n \n Instructor: <\/span>Amanda Evans\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n What is the relationship between our conscious experience and the physical world? This question is one way of articulating the \u201cmind-body problem\u201d, which is arguably the central question of the philosophy of mind. This course provides a survey of the most influential historical and contemporary answers to the mind-body problem, including dualism, behaviorism, identity theory, and functionalism. Toward the end of the term, we will likely also cover topics such as panpsychism, the Simulation Argument, the puzzle of personal identity, and the question of whether a machine could ever be conscious. Students will leave the course with a firm foundation in the philosophy of mind and with a sharper perspective on what it means to be a thinking being.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Humanities\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

PSYC 244-01 10609<\/span><\/td>\n Cognitive Neuroscience<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span>THEATR 001\n <\/td>\n \n Instructor: <\/span>Darcy Burgund\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Permission of instructor required*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n Cognitive neuroscience is a relatively recent discipline that combines cognitive science and cognitive psychology with biology and neuroscience to investigate how the brain enables the myriad of complex functions we know as the mind. This course will explore basic concepts and contemporary topics in the field, focusing in particular on the methods used in cognitive neuroscience research. Through lecture and lab sessions, students will learn to read and interpret primary source material, design and implement cognitive neuroscience studies, and present research in verbal and written forms. Overall, students will gain an appreciation for the amazing intricacy of the brain-mind relationship, as well as a sense of how this relationship may be understood eventually using cognitive neuroscience techniques. Group A course. Prerequisite(s): PSYC 100.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n Quantitative Thinking Q1\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

PSYC 254-01 10615<\/span><\/td>\n Social Psychology<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span>ARTCOM 202\n <\/td>\n \n Instructor: <\/span>Grace Vieth\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course\u00a0survey the ways in which social phenomena influence the thoughts, feelings, and behavior of individuals. We\u00a0examine the\u00a0major theories, experiments, and issues in the field of\u00a0social psychology. Sample topics include emotion, aggression, conformity, attitudes, altruism,\u00a0prejudice, persuasion, and group dynamics. Group B course. Prerequisite(s): PSYC 100.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

POLI 269-01 10574<\/span><\/td>\n Empirical Research Methods<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>01:20 pm-02:50 pm\n <\/td>\n \n Room: <\/span>CARN 206\n <\/td>\n \n Instructor: <\/span>Julie Dolan\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course will equip you with the skills and intuition to think about politics in a more critical and organized way. \u00a0You will practice the scientific method - identifying a problem worthy of study, developing testable hypotheses, designing a research strategy, gathering data, analyzing data, and interpreting your results - and contemplate the philosophical conundrums that underlie our efforts to describe, explain, and interpret complex phenomena.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

ECON 361-01 10228<\/span><\/td>\n Intermediate Microeconomic Analysis<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>09:40 am-11:10 am\n <\/td>\n \n Room: <\/span>CARN 06A\n <\/td>\n \n Instructor: <\/span>Sarah West\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n In microeconomics, we study how individuals make economic choices. We pose questions like: What influences consumers' purchasing decisions? Will firms replace employees with machines if the minimum wage increases? How do concentrated markets (e.g., airlines, Amazon) affect consumers? If a firm can set different prices for different consumers, what set of prices will be optimal? How do risk and uncertainty affect investment decisions? What is the efficient amount of pollution? What role should governments play in providing resources for public goods like parks? In this course, we derive models of constrained choice to examine fundamental tradeoffs faced by individuals, firms, and governments when making economic decisions and to determine how such decisions influence market outcomes and well-being. This course counts as a Group E\u00a0elective. Prerequisite(s): MATH 135\u00a0or\u00a0MATH 137, and one 200-level Economics course from Group E electives. Not open to first-year students except by permission of the instructor. C- or higher required for all prerequisites.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

ECON 361-02 10229<\/span><\/td>\n Intermediate Microeconomic Analysis<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>01:20 pm-02:50 pm\n <\/td>\n \n Room: <\/span>THEATR 202\n <\/td>\n \n Instructor: <\/span>Mark Drozd\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n In microeconomics, we study how individuals make economic choices. We pose questions like: What influences consumers' purchasing decisions? Will firms replace employees with machines if the minimum wage increases? How do concentrated markets (e.g., airlines, Amazon) affect consumers? If a firm can set different prices for different consumers, what set of prices will be optimal? How do risk and uncertainty affect investment decisions? What is the efficient amount of pollution? What role should governments play in providing resources for public goods like parks? In this course, we derive models of constrained choice to examine fundamental tradeoffs faced by individuals, firms, and governments when making economic decisions and to determine how such decisions influence market outcomes and well-being. This course counts as a Group E\u00a0elective. Prerequisite(s): MATH 135\u00a0or\u00a0MATH 137, and one 200-level Economics course from Group E electives. Not open to first-year students except by permission of the instructor. C- or higher required for all prerequisites.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

COMP 440-01 10208<\/span><\/td>\n Collective Intelligence<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>01:20 pm-02:50 pm\n <\/td>\n \n Room: <\/span>OLRI 241\n <\/td>\n \n Instructor: <\/span>Shilad Sen\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course introduces the theory and practice of data science applied to online communities such as Wikipedia, Facebook, and Twitter. Students will read and discuss recent academic research papers that analyze behavior on these websites and use computational simulation, machine learning, and data-mining techniques to analyze massive behavioral datasets in areas such as recommender systems, natural language processing, and tagging systems. This course counts as the capstone. Prerequisite(s): COMP 221; or both COMP 127 and STAT 253\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

COMP 484-01 10211<\/span><\/td>\n Introduction to Artificial Intelligence<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>03:30 pm-04:30 pm\n <\/td>\n \n Room: <\/span>OLRI 258\n <\/td>\n \n Instructor: <\/span>Susan Fox\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*3 seats saved for juniors*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n An introduction to the basic principles and techniques of artificial intelligence. Topics will include specific AI techniques, a range of application areas, and connections between AI and other areas of study (i.e., philosophy, psychology). Techniques may include heuristic search, automated reasoning, machine learning, deliberative planning and behavior-based agent control. Application areas include robotics, games, knowledge representation, and natural language processing. This course involves programming in Python; students should have a basic familiarity with Python or be prepared to learn Python during the course. This course counts as the capstone. Prerequisite(s): COMP 221; or (COMP 127 and STAT 253); or permission of instructor.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Writing WP\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n <\/tbody>\n <\/table>\n<\/div>\n\n \n\n \n\t

Spring 2027<\/h2>\n

Visit the Registrar's Class Schedule for live registration information<\/a><\/p>\n

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Num. \/ Sec. \/ CRN<\/th>\n Name<\/th>\n Days<\/th>\n Time<\/th>\n Room<\/th>\n Instructor<\/th>\n <\/th>\n \n <\/tr>\n <\/thead>\n
LING 100-01 30427<\/span><\/td>\n Introduction to Linguistics<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Zuoyu Tian\n <\/td>\n \n \n <\/td>\n <\/tr>\n
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<\/p>\n

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\n \n Details\n <\/a>\n
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\n The aim of this course is to make you aware of the complex organization and systematic nature of language. You will be studying yourself and those around you, since you are all language users. Most of your knowledge of language, however, is unconscious, and earlier education may have given you confusing or misleading notions about language. This course is intended to clarify your ideas about language and bring you to a better understanding of its nature. You will learn more about how languages work by observing and analyzing\u00a0 language(s) (from people you know, from yourself, videos, in-class data sets, social media, texting etc.)\u00a0 By the end of the course you should be familiar with some of the terminology and techniques of linguistic analysis and be able to apply this knowledge to the description of different languages.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Writing WP\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

PSYC 100-01 30556<\/span><\/td>\n Introduction to Psychology<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Keira Leneman\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

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\n \n Details\n <\/a>\n
\n

\n An introduction to psychological science -- the study of behavior and mental processes. This course surveys the major subdisciplines of the field, including such topics as the brain and neuroscience, behavioral genetics, cognitive and social development, perception, learning, memory, decision-making, language, consciousness, emotions, motivation, psychological disorders, social identity, interpersonal interactions and group and\u00a0cultural processes. Lecture and laboratory components.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Writing WP\n \n \n Quantitative Thinking Q1\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

PSYC 100-02 30557<\/span><\/td>\n Introduction to Psychology<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>01:10 pm-02:10 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Keira Leneman\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n An introduction to psychological science -- the study of behavior and mental processes. This course surveys the major subdisciplines of the field, including such topics as the brain and neuroscience, behavioral genetics, cognitive and social development, perception, learning, memory, decision-making, language, consciousness, emotions, motivation, psychological disorders, social identity, interpersonal interactions and group and\u00a0cultural processes. Lecture and laboratory components.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Writing WP\n \n \n Quantitative Thinking Q1\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

PHIL 111-01 30500<\/span><\/td>\n Introduction to Symbolic Logic<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>09:40 am-11:10 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Bixin Guo\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

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\n \n Details\n <\/a>\n
\n

\n An introduction to formal methods for evaluating deductive arguments. Topics include formal fallacies, decision procedures, translation of arguments to argument forms, and natural deduction proofs in propositional and predicate logic.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q1\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

PHIL 111-02 30501<\/span><\/td>\n Introduction to Symbolic Logic<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>03:00 pm-04:30 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Bixin Guo\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n An introduction to formal methods for evaluating deductive arguments. Topics include formal fallacies, decision procedures, translation of arguments to argument forms, and natural deduction proofs in propositional and predicate logic.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q1\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

COMP 112-01 30167<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>12:00 pm-01:00 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Susan Fox\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with STAT 112-01 (30168); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 112-01 30168<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>12:00 pm-01:00 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Susan Fox\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 112-01 (30167); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

COMP 112-02 30169<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>01:10 pm-02:10 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Susan Fox\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with STAT 112-02 (30170); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 112-02 30170<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>01:10 pm-02:10 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Susan Fox\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 112-02 (30169); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

COMP 112-03 30171<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>01:20 pm-02:50 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Lauren Milne\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with STAT 112-03 (30172); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 112-03 30172<\/span><\/td>\n Introduction to Data Science<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>01:20 pm-02:50 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Lauren Milne\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 112-03 (30171); seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course provides an introduction to the handling, analysis, and interpretation of the big datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

ECON 119-01 30200<\/span><\/td>\n Principles of Economics<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>08:00 am-09:30 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Mario Solis-Garcia\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n Economics is the study of how people make decisions and how these decisions apply to real-world problems. Economics can help us understand income inequality within and across countries, the quality of the environment, unemployment, poverty, crime, health care, financial crises, technological change, inflation and many more issues. This course introduces the basic tools that economists use to explore these topics and will cover fundamental economic concepts like scarcity, supply and demand, costs and benefits, trade-offs, and incentives. This course is a one semester overview of both microeconomics (the study of choices firms and individual consumers make) and macroeconomics (the study of the economy as a whole).\u00a0This course counts as a Group E economics course and is the prerequisite for 200-level E courses in the Economics Department.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

ECON 119-02 30201<\/span><\/td>\n Principles of Economics<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>09:40 am-11:10 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Mario Solis-Garcia\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n Economics is the study of how people make decisions and how these decisions apply to real-world problems. Economics can help us understand income inequality within and across countries, the quality of the environment, unemployment, poverty, crime, health care, financial crises, technological change, inflation and many more issues. This course introduces the basic tools that economists use to explore these topics and will cover fundamental economic concepts like scarcity, supply and demand, costs and benefits, trade-offs, and incentives. This course is a one semester overview of both microeconomics (the study of choices firms and individual consumers make) and macroeconomics (the study of the economy as a whole).\u00a0This course counts as a Group E economics course and is the prerequisite for 200-level E courses in the Economics Department.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

ECON 119-03 30202<\/span><\/td>\n Principles of Economics<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Alexander Von Hafften\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n Economics is the study of how people make decisions and how these decisions apply to real-world problems. Economics can help us understand income inequality within and across countries, the quality of the environment, unemployment, poverty, crime, health care, financial crises, technological change, inflation and many more issues. This course introduces the basic tools that economists use to explore these topics and will cover fundamental economic concepts like scarcity, supply and demand, costs and benefits, trade-offs, and incentives. This course is a one semester overview of both microeconomics (the study of choices firms and individual consumers make) and macroeconomics (the study of the economy as a whole).\u00a0This course counts as a Group E economics course and is the prerequisite for 200-level E courses in the Economics Department.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

ECON 119-04 30203<\/span><\/td>\n Principles of Economics<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>10:50 am-11:50 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Alexander Von Hafften\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n Economics is the study of how people make decisions and how these decisions apply to real-world problems. Economics can help us understand income inequality within and across countries, the quality of the environment, unemployment, poverty, crime, health care, financial crises, technological change, inflation and many more issues. This course introduces the basic tools that economists use to explore these topics and will cover fundamental economic concepts like scarcity, supply and demand, costs and benefits, trade-offs, and incentives. This course is a one semester overview of both microeconomics (the study of choices firms and individual consumers make) and macroeconomics (the study of the economy as a whole).\u00a0This course counts as a Group E economics course and is the prerequisite for 200-level E courses in the Economics Department.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

COMP 123-01 30175<\/span><\/td>\n Core Concepts in Computer Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Amin Alhashim\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course introduces the field of computer science, including central concepts such as the design and implementation of algorithms and programs, testing and analyzing programs, the representation of information within the computer, and the role of abstraction and metaphor in computer science. The exploration of these central ideas will draw examples from a range of application areas including multimedia processing, turtle graphics, and text processing. Course work will use the Python programming language.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

COMP 123-02 30176<\/span><\/td>\n Core Concepts in Computer Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>10:50 am-11:50 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Lian Duan\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course introduces the field of computer science, including central concepts such as the design and implementation of algorithms and programs, testing and analyzing programs, the representation of information within the computer, and the role of abstraction and metaphor in computer science. The exploration of these central ideas will draw examples from a range of application areas including multimedia processing, turtle graphics, and text processing. Course work will use the Python programming language.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

COMP 123-03 30177<\/span><\/td>\n Core Concepts in Computer Science<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>08:30 am-09:30 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Lian Duan\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course introduces the field of computer science, including central concepts such as the design and implementation of algorithms and programs, testing and analyzing programs, the representation of information within the computer, and the role of abstraction and metaphor in computer science. The exploration of these central ideas will draw examples from a range of application areas including multimedia processing, turtle graphics, and text processing. Course work will use the Python programming language.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-01 30660<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Brianna Heggeseth\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, data visualizations, multivariate linear regression, logistic regression, probability, model building and interpretation (i.e., confounding variables, causal diagrams, data context), and statistical inference (i.e., confidence intervals and hypothesis testing).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-02 30661<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>10:50 am-11:50 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Brianna Heggeseth\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, data visualizations, multivariate linear regression, logistic regression, probability, model building and interpretation (i.e., confounding variables, causal diagrams, data context), and statistical inference (i.e., confidence intervals and hypothesis testing).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-03 30662<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>01:10 pm-02:10 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Brianna Heggeseth\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, data visualizations, multivariate linear regression, logistic regression, probability, model building and interpretation (i.e., confounding variables, causal diagrams, data context), and statistical inference (i.e., confidence intervals and hypothesis testing).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-04 30663<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>08:30 am-09:30 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Abby Loe\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, data visualizations, multivariate linear regression, logistic regression, probability, model building and interpretation (i.e., confounding variables, causal diagrams, data context), and statistical inference (i.e., confidence intervals and hypothesis testing).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

STAT 155-05 30664<\/span><\/td>\n Introduction to Statistical Modeling<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>03:00 pm-04:30 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Alex Knutson\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Seats saved for: 4 seniors, 6 juniors, 8 sophomores, 6 First-Years*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, data visualizations, multivariate linear regression, logistic regression, probability, model building and interpretation (i.e., confounding variables, causal diagrams, data context), and statistical inference (i.e., confidence intervals and hypothesis testing).\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

PSYC 201-01 30562<\/span><\/td>\n Research in Psychology I<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>01:10 pm-02:10 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Violet Brown\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course is an introduction to the basic principles of research in psychology, with an emphasis on statistical techniques used in psychological science.\u00a0We examine how to test psychological hypotheses using various statistical analyses, and we consider the pros and cons of experimental, quasi-experimental, and correlational research designs.\u00a0The course includes a weekly laboratory component in which students develop proficiency with statistical software, writing reports in American Psychological Association style, and familiarity with experimental techniques unique to behavioral research. Prerequisite(s): PSYC 100\u00a0Permission of instructor is required for first year students.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

EDUC 220-01 30221<\/span><\/td>\n Educational Psychology<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Cristina Silva Gleason\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with PSYC 220-01 (30222)*<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n An introduction to theory and research in educational psychology. Topics include learning theory, learner characteristics, intelligence, creativity, motivation, measurement and evaluation, and models of teaching appropriate for diverse learners from early childhood through young adulthood. Students are required to complete observations in classroom settings.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q1\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

PSYC 242-01 30565<\/span><\/td>\n Cognitive Psychology<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>01:10 pm-02:10 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Erika DeAngelis\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n How do people learn, remember, and think?\u00a0\u00a0How much of our cognitive life are we even consciously aware of?\u00a0\u00a0This course addresses these questions and others from the perspective of experimental cognitive\u00a0psychology.\u00a0\u00a0Topics include perception, attention, memory, the organization of knowledge, language, and decision making. Weekly laboratory sessions afford students the opportunity to interact more directly with cognitive phenomena and research methods.\u00a0\u00a0Readings are mainly from primary sources.\u00a0 Group A course. Prerequisite(s): PSYC 100.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q2\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

PSYC 244-01 30567<\/span><\/td>\n Cognitive Neuroscience<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>09:40 am-10:40 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Darcy Burgund\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Permission of instructor required*<\/p>\n

\n
\n \n Details\n <\/a>\n
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\n Cognitive neuroscience combines cognitive science and cognitive psychology with biology and neuroscience to investigate how the brain enables the myriad of complex functions we know as the mind. This course will explore basic concepts and contemporary topics in the field, focusing in particular on the methods used in cognitive neuroscience research. Through lecture and lab sessions, students will learn to read and interpret primary source material, design and implement cognitive neuroscience studies, and present research in verbal and written forms. Overall, students will gain an appreciation for the amazing intricacy of the brain-mind relationship, as well as a sense of how this relationship may be understood eventually using cognitive neuroscience techniques. Prerequisite(s): PSYC 100.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q1\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Natural science and mathematics\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

PSYC 254-01 30571<\/span><\/td>\n Social Psychology<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>09:40 am-11:10 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Melina Singh\n <\/td>\n \n \n <\/td>\n <\/tr>\n
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<\/p>\n

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\n \n Details\n <\/a>\n
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\n This course\u00a0survey the ways in which social phenomena influence the thoughts, feelings, and behavior of individuals. We\u00a0examine the\u00a0major theories, experiments, and issues in the field of\u00a0social psychology. Sample topics include emotion, aggression, conformity, attitudes, altruism,\u00a0prejudice, persuasion, and group dynamics. Group B course. Prerequisite(s): PSYC 100.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

POLI 269-01 30541<\/span><\/td>\n Empirical Research Methods<\/td>\n \n Days: <\/span>M W F \n <\/td>\n \n Time: <\/span>02:20 pm-03:20 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Lisa Mueller\n <\/td>\n \n \n <\/td>\n <\/tr>\n
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<\/p>\n

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\n \n Details\n <\/a>\n
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\n This course will equip you with the skills and intuition to think about politics in a more critical and organized way. \u00a0You will practice the scientific method - identifying a problem worthy of study, developing testable hypotheses, designing a research strategy, gathering data, analyzing data, and interpreting your results - and contemplate the philosophical conundrums that underlie our efforts to describe, explain, and interpret complex phenomena.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

SOCI 269-01 30623<\/span><\/td>\n Social Science Inquiry<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>01:20 pm-02:50 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Christina Hughes\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n Social scientists have many methodological approaches to choose from in their research toolkit ranging from qualitative to quantitative to comparative historical. Each of these approaches allows us to see and understand society from different perspectives, sometimes offering us complementary snapshots and other times presenting us with contradictory information. How, then, can we engage evidence to make claims about the social world? What even counts as evidence and who gets to speak for whom? Social Science Inquiry is part of a methods training sequence that emphasizes quantitative social science inquiry. The course covers quantitative social science as an epistemology compared to other ways of knowing, engages with published quantitative work in the field as well as its critics, teaches the basics of quantitative analysis for the social sciences, and largely emphasizes a hands-on approach to conceptualizing and implementing a research project. Also covering themes related to research ethics, these topics will be approached from a non-extractive lens, emphasizing the need to situate our research in the needs, obligations, and relations asked of us by the broader community. Note: The course uses R and counts as a Q3 and WA requirement.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Writing WA\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

ECON 361-01 30206<\/span><\/td>\n Intermediate Microeconomic Analysis<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>09:40 am-11:10 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Mark Drozd\n <\/td>\n \n \n <\/td>\n <\/tr>\n
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<\/p>\n

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\n \n Details\n <\/a>\n
\n

\n In microeconomics, we study how individuals make economic choices. We pose questions like: What influences consumers' purchasing decisions? Will firms replace employees with machines if the minimum wage increases? How do concentrated markets (e.g., airlines, Amazon) affect consumers? If a firm can set different prices for different consumers, what set of prices will be optimal? How do risk and uncertainty affect investment decisions? What is the efficient amount of pollution? What role should governments play in providing resources for public goods like parks? In this course, we derive models of constrained choice to examine fundamental tradeoffs faced by individuals, firms, and governments when making economic decisions and to determine how such decisions influence market outcomes and well-being. This course counts as a Group E\u00a0elective. Prerequisite(s): MATH 135\u00a0or\u00a0MATH 137, and one 200-level Economics course from Group E electives. Not open to first-year students except by permission of the instructor. C- or higher required for all prerequisites.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

ECON 361-02 30207<\/span><\/td>\n Intermediate Microeconomic Analysis<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>01:20 pm-02:50 pm\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Mark Drozd\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n In microeconomics, we study how individuals make economic choices. We pose questions like: What influences consumers' purchasing decisions? Will firms replace employees with machines if the minimum wage increases? How do concentrated markets (e.g., airlines, Amazon) affect consumers? If a firm can set different prices for different consumers, what set of prices will be optimal? How do risk and uncertainty affect investment decisions? What is the efficient amount of pollution? What role should governments play in providing resources for public goods like parks? In this course, we derive models of constrained choice to examine fundamental tradeoffs faced by individuals, firms, and governments when making economic decisions and to determine how such decisions influence market outcomes and well-being. This course counts as a Group E\u00a0elective. Prerequisite(s): MATH 135\u00a0or\u00a0MATH 137, and one 200-level Economics course from Group E electives. Not open to first-year students except by permission of the instructor. C- or higher required for all prerequisites.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Quantitative Thinking Q3\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

\n \n Course Materials<\/strong>\n <\/a>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/td>\n <\/tr>\n \n

PSYC 377-01 30584<\/span><\/td>\n Moral Psychology<\/td>\n \n Days: <\/span> T R \n <\/td>\n \n Time: <\/span>09:40 am-11:10 am\n <\/td>\n \n Room: <\/span> \n <\/td>\n \n Instructor: <\/span>Steve Guglielmo\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

\n
\n \n Details\n <\/a>\n
\n

\n This course\u00a0explores how and why we make moral judgments about people and their behavior. How are our moral judgments shaped by intuition, emotion, and reasoning? What are the moral implications of climate change? Do we ever put the interests of our broader group or community above our own self-interest? How do we balance punishment motives of retribution and deterrence, and how do these relate to policy decisions about capital punishment? Could a robot have moral rights and responsibilities? We will examine these questions by considering theories and findings from social, developmental, evolutionary, and political psychology, as well as from related fields like philosophy and artificial intelligence. Prerequisite(s): PSYC 201\u00a0and one intermediate psychology course, or permission of instructor.\n <\/p>\n

\n General Education Requirements:<\/strong>\n \n \n Writing WA\n \n \n <\/p>\n

\n Distribution Requirements:<\/strong>\n \n \n Social science\n \n \n <\/p>\n

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