{"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 Spring 2026<\/a>\n \n \n Fall 2026<\/a>\n \n \n\t\n\t\n\t\n <\/p>\n \n\t

Spring 2026<\/h2>\n

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

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
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 32398<\/span><\/td>\n Introduction to Linguistics<\/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>HUM 314\n <\/td>\n \n Instructor: <\/span>Zuoyu Tian\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Registration limit will be adjusted to save 10 seats for First Years*<\/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 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 32586<\/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>OLRI 100\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

PSYC 100-02 32587<\/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>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

PSYC 100-L1 32588<\/span><\/td>\n Introduction to Psychology Lab<\/td>\n \n Days: <\/span> T \n <\/td>\n \n Time: <\/span>09:40 am-11:10 am\n <\/td>\n \n Room: <\/span>OLRI 352\n <\/td>\n \n Instructor: <\/span>Jamie Atkins\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 <\/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-L2 32589<\/span><\/td>\n Introduction to Psychology Lab<\/td>\n \n Days: <\/span> T \n <\/td>\n \n Time: <\/span>03:00 pm-04:30 pm\n <\/td>\n \n Room: <\/span>OLRI 352\n <\/td>\n \n Instructor: <\/span>Jamie Atkins\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 <\/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-L3 32590<\/span><\/td>\n Introduction to Psychology Lab<\/td>\n \n Days: <\/span> R \n <\/td>\n \n Time: <\/span>09:40 am-11:10 am\n <\/td>\n \n Room: <\/span>OLRI 352\n <\/td>\n \n Instructor: <\/span>Jamie Atkins\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 <\/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-L4 32591<\/span><\/td>\n Introduction to Psychology Lab<\/td>\n \n Days: <\/span> R \n <\/td>\n \n Time: <\/span>01:20 pm-02:50 pm\n <\/td>\n \n Room: <\/span>OLRI 352\n <\/td>\n \n Instructor: <\/span>Jamie Atkins\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 <\/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 32421<\/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>OLRI 254\n <\/td>\n \n Instructor: <\/span>Leslie Myint\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with STAT 112-01 (32422); Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for 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 32422<\/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>OLRI 254\n <\/td>\n \n Instructor: <\/span>Leslie Myint\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 112-01 (32421); Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for 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 32423<\/span><\/td>\n Introduction to Data Science<\/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>OLRI 254\n <\/td>\n \n Instructor: <\/span>Leslie Myint\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with STAT 112-02 (32424); Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for 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 32424<\/span><\/td>\n Introduction to Data Science<\/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>OLRI 254\n <\/td>\n \n Instructor: <\/span>Leslie Myint\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 112-02 (32423); Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for 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 32425<\/span><\/td>\n Introduction to Data Science<\/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>OLRI 254\n <\/td>\n \n Instructor: <\/span>Dan Drake\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with STAT 112-03 (32426); Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for 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 32426<\/span><\/td>\n Introduction to Data Science<\/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>OLRI 254\n <\/td>\n \n Instructor: <\/span>Dan Drake\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with COMP 112-03 (32425);Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores, and 6 seats for 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 32165<\/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>ARTCOM 202\n <\/td>\n \n Instructor: <\/span>Maren Young\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 32166<\/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>ARTCOM 202\n <\/td>\n \n Instructor: <\/span>Maren Young\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 32167<\/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>THEATR 203\n <\/td>\n \n Instructor: <\/span>Thomas Durfee\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 32427<\/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>OLRI 258\n <\/td>\n \n Instructor: <\/span>Dan Drake\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Registration limit will be adjusted to save 4 seats for First Years*<\/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 32428<\/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>OLRI 258\n <\/td>\n \n Instructor: <\/span>Dan Drake\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Registration limit will be adjusted to save 4 seats for First Years*<\/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 32429<\/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>Lian Duan\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Registration limit will be adjusted to save 4 seats for First Years*<\/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 123-04 32430<\/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>Lian Duan\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Registration limit will be adjusted to save 4 seats for First Years*<\/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 32475<\/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>THEATR 200\n <\/td>\n \n Instructor: <\/span>Brianna Heggeseth\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required; Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores and 6 seats for 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 32476<\/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>THEATR 200\n <\/td>\n \n Instructor: <\/span>Brianna Heggeseth\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required; Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores and 6 seats for 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 32477<\/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 202\n <\/td>\n \n Instructor: <\/span>Jedidiah Carlson\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required; Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores and 6 seats for 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 32478<\/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 202\n <\/td>\n \n Instructor: <\/span>Jedidiah Carlson\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required; Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores and 6 seats for 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 32479<\/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 202\n <\/td>\n \n Instructor: <\/span>Alicia Johnson\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required; Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores and 6 seats for 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 32480<\/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>THEATR 203\n <\/td>\n \n Instructor: <\/span>Md Mutasim Billah\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required; Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores and 6 seats for 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 155-07 32481<\/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>THEATR 203\n <\/td>\n \n Instructor: <\/span>Md Mutasim Billah\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required; Registration limit will be adjusted to save 4 seats for Seniors, 6 seats for Juniors, 8 seats for Sophomores and 6 seats for 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 32592<\/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>THEATR 002\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 32593<\/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

PSYC 201-L1 32594<\/span><\/td>\n Research in Psychology I Lab<\/td>\n \n Days: <\/span> R \n <\/td>\n \n Time: <\/span>09:40 am-11:10 am\n <\/td>\n \n Room: <\/span>OLRI 349\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 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 <\/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-L2 32595<\/span><\/td>\n Research in Psychology I Lab<\/td>\n \n Days: <\/span> R \n <\/td>\n \n Time: <\/span>03:00 pm-04:30 pm\n <\/td>\n \n Room: <\/span>OLRI 349\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 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 <\/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-L3 32596<\/span><\/td>\n Research in Psychology I Lab<\/td>\n \n Days: <\/span> T \n <\/td>\n \n Time: <\/span>08:00 am-09:30 am\n <\/td>\n \n Room: <\/span>OLRI 349\n <\/td>\n \n Instructor: <\/span>Ariel James\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 <\/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-L4 32597<\/span><\/td>\n Research in Psychology I Lab<\/td>\n \n Days: <\/span> T \n <\/td>\n \n Time: <\/span>09:40 am-11:10 am\n <\/td>\n \n Room: <\/span>OLRI 349\n <\/td>\n \n Instructor: <\/span>Ariel James\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 <\/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 32599<\/span><\/td>\n Educational 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>ARTCOM 202\n <\/td>\n \n Instructor: <\/span>Cristina Silva Gleason\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*First day attendance required; cross-listed with PSYC 220-01 (32598); Registration limit will be adjusted to save 5 seats for First Years*<\/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 32600<\/span><\/td>\n Cognitive 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>OLRI 352\n <\/td>\n \n Instructor: <\/span>Brooke Lea\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 242-L1 32601<\/span><\/td>\n Cognitive Psychology Lab<\/td>\n \n Days: <\/span> R \n <\/td>\n \n Time: <\/span>01:20 pm-02:50 pm\n <\/td>\n \n Room: <\/span>OLRI 349\n <\/td>\n \n Instructor: <\/span>Brooke Lea\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 <\/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 32602<\/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 205\n <\/td>\n \n Instructor: <\/span>Darcy Burgund\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/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 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 244-L1 32603<\/span><\/td>\n Cognitive Neuroscience Lab<\/td>\n \n Days: <\/span> R \n <\/td>\n \n Time: <\/span>09:40 am-11:10 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

<\/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 <\/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 32608<\/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>OLRI 301\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 32573<\/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>CARN 305\n <\/td>\n \n Instructor: <\/span>Lisa Mueller\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. Empirical Methods: The department requires its majors to take one course in empirical research methodology\u00a0before\u00a0their senior year. In addition to this course, there are a number of other courses that fulfill this requirement, including:\u00a0POLI 272,\u00a0SOCI 269,\u00a0SOCI 270,\u00a0SOCI 275. In some cases, research methods courses taken in other social science disciplines may be used to fulfill this requirement following approval by the political science department chair.\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 32653<\/span><\/td>\n Social Science Inquiry<\/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 204\n <\/td>\n \n Instructor: <\/span>Erik Larson\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-that is, the approach to social science research that entails statistically analyzing relatively large datasets and emphasizes concerns such as measurement, replicability, objectivity, generalizability, and causal inference. However, this methodological approach is not without its critics. Therefore, this course begins by being in conversation with quantitative social science's critics, examining the abuse of quantitative knowledge in social science research and how quantitative social scientists have themselves been complicit in building many of the tools now at the center of contemporary ethical debates over online privacy, digitization, facial recognition, AI, and their use in policing, surveillance, and border enforcement. Taking the stance that quantitative methods are tools with the capacity to either build up or break down social structures, we will work with municipal data from the City of Minneapolis to explore how quantitative methods may be, in sociologist Ruha Benjamin's words, "retooled" to serve emancipatory and abolitionist purposes. Readings will focus on case studies from social science research and critical texts from science and technology studies. Methods-training content will specifically entail the use of R and will cover inferring population characteristics from survey samples, estimating causality and predicting outcomes using linear regression, the principles of probability and estimating statistical uncertainty, and how to critically read quantitative information presented via figures and tables.\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

LING 304-01 32406<\/span><\/td>\n Experimental Phonetics<\/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 216\n <\/td>\n \n Instructor: <\/span>Christina Esposito\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

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

\n What is sound and how do linguists study it? \u00a0This course is an introduction to basic acoustics and experimental procedures in linguistics. \u00a0We learn how to quantify the speech signal (using Praat software) to answer questions about human speech sounds. Students will have a chance to learn about their own speech through in-class activities, such as plotting their vowel space, measuring their pitch range, and determining how they produce consonants by conducting static palatography (a procedure where the tongue is "painted" to learn more about articulation). Students will conduct their own linguistic research based on a language\/dialect of their choice. Prerequisite(s): LING 104\n <\/p>\n

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

PHIL 310-01 32538<\/span><\/td>\n Philosophy of Science<\/td>\n \n Days: <\/span>M W \n <\/td>\n \n Time: <\/span>02:20 pm-04:30 pm\n <\/td>\n \n Room: <\/span>MAIN 011\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 Science, and the technologies it fuels, are at the heart of modern society. It shapes how we see and interact with the world. But what is science, really? How does it work, and just how seriously should we take its discoveries? This course dives into the questions behind the sciences we often take for granted. More specifically, we will discuss: What is the line between science and pseudo-science? Should we accept as true the weird and wonderful things that modern science seems to be telling us about the Universe\u2014quarks, DNA molecules, distant galaxies, parallel universes\u2014or are scientific theories our most handy tools to navigate the world we observe with our senses? We will also explore how alternative perspectives, such as feminist philosophy of science, challenge the traditional idea that science is objective and value-free, and how the practice of science has been influenced by its social and historical contexts? No prerequisites in sciences. While this class is in a longer time slot, it will meet for the normal three hours per week.\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

PHIL 314-01 32539<\/span><\/td>\n Contemporary Metaphysics<\/td>\n \n Days: <\/span> W \n <\/td>\n \n Time: <\/span>07:00 pm-10:00 pm\n <\/td>\n \n Room: <\/span>MAIN 009\n <\/td>\n \n Instructor: <\/span>Geoffrey Gorham\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/p>\n

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

\n Metaphysics - the investigation of the fundamental categories of being - is as old as philosophy itself. This class is devoted to the most important and intriguing metaphysical theories and problems of contemporary philosophy. Specific topics will vary from year to year depending on student and faculty interest but will likely include: time and space, freedom and determinism, infinity, identity, causality, death, the origin and demise of the universe, abstract and fictional objects, necessity and possibility, human nature and transhumanism, and the categories of race and gender. We will also be concerned with the relationship between metaphysics and other domains within and beyond philosophy, such as literature and natural science. Classes will be seminar-style, focused on discussion of readings and student work. Prerequisite(s): A 100- or 200- level Philosophy course.\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 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

ECON 361-01 32179<\/span><\/td>\n Intermediate Microeconomic Analysis<\/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 304\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 32180<\/span><\/td>\n Intermediate Microeconomic Analysis<\/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 304\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

PSYC 378-01 32616<\/span><\/td>\n Psychology of Language<\/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>THEATR 203\n <\/td>\n \n Instructor: <\/span>Ali Fulsher\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

*Cross-listed with LING 378-01 (32617)*<\/p>\n

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

\n An examination of the psychological factors related to written and spoken language. This course takes an interdisciplinary approach, integrating readings from linguistics, philosophy, cognitive psychology, social psychology, and anthropology. Topics include the nature and origin of language, mental processes underlying language comprehension and production, the relationship between language and thought, and socio-cultural contexts surrounding language use. Students will also learn about classic and contemporary research methods to propose an original study for their final project.\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 <\/tbody>\n <\/table>\n<\/div>\n\n \n\n \n\t

Fall 2026<\/h2>\n

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

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
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> \n <\/td>\n \n Instructor: <\/span>Morgan Sleeper\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/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> \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 Quantitative Thinking Q1\n \n \n Writing WA\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 100-02 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> \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 Quantitative Thinking Q1\n \n \n Writing WA\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

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> \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> \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> \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> \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> \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> \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> \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> \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> \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> \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> \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> \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> \n <\/td>\n \n Instructor: <\/span>STAFF\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> \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> \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> \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> \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> \n <\/td>\n \n Instructor: <\/span>STAFF\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> \n <\/td>\n \n Instructor: <\/span>STAFF\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> \n <\/td>\n \n Instructor: <\/span>Lori Ziegelmeier\n <\/td>\n \n \n <\/td>\n <\/tr>\n
\n

<\/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> \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> \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> \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> \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> \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> \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> \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. Empirical Methods: The department requires its majors to take one course in empirical research methodology\u00a0before\u00a0their senior year. In addition to this course, there are a number of other courses that fulfill this requirement, including:\u00a0POLI 272,\u00a0SOCI 269,\u00a0SOCI 270,\u00a0SOCI 275. In some cases, research methods courses taken in other social science disciplines may be used to fulfill this requirement following approval by the political science department chair.\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> \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> \n <\/td>\n \n Instructor: <\/span>STAFF\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> \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> \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 \n \n\n \n\n \n<\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"open","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-27","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.macalester.edu\/cognitive-science\/wp-json\/wp\/v2\/pages\/27","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.macalester.edu\/cognitive-science\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.macalester.edu\/cognitive-science\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.macalester.edu\/cognitive-science\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.macalester.edu\/cognitive-science\/wp-json\/wp\/v2\/comments?post=27"}],"version-history":[{"count":1,"href":"https:\/\/www.macalester.edu\/cognitive-science\/wp-json\/wp\/v2\/pages\/27\/revisions"}],"predecessor-version":[{"id":137,"href":"https:\/\/www.macalester.edu\/cognitive-science\/wp-json\/wp\/v2\/pages\/27\/revisions\/137"}],"wp:attachment":[{"href":"https:\/\/www.macalester.edu\/cognitive-science\/wp-json\/wp\/v2\/media?parent=27"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}