PSCI1800 - Introduction to Data Science

Status
A
Activity
REC
Section number integer
4
Title (text only)
Introduction to Data Science
Term
2023A
Subject area
PSCI
Section number only
004
Section ID
PSCI1800004
Course number integer
1800
Meeting times
F 10:15 AM-11:14 AM
Meeting location
BENN 323
Level
undergraduate
Description
Understanding and interpreting large, quantitative data sets is increasingly central in political and social science. Whether one seeks to understand political communication, international trade, inter-group conflict, or other issues, the availability of large quantities of digital data has revolutionized the study of politics. Nonetheless, most data-related courses focus on statistical estimation, rather than on the related but distinctive problems of data acquisition, management and visualization--in a term, data science. This course addresses that imbalance by focusing squarely on data science. Leaving this course, students will be able to acquire, format, analyze, and visualize various types of political data using the statistical programming language R. This course is not a statistics class, but it will increase the capacity of students to thrive in future statistics classes. While no background in statistics or political science is required, students are expected to be generally familiar with contemporary computing environments (e.g. know how to use a computer) and have a willingness to learn a variety of data science tools. You are encouraged (but certainly not required) to register for both this course and PSCI 338 at the same time, as the courses cover distinct, but complimentary material.
Course number only
1800
Fulfills
Quantitative Data Analysis
Use local description
No

PSCI1800 - Introduction to Data Science

Status
A
Activity
REC
Section number integer
3
Title (text only)
Introduction to Data Science
Term
2023A
Subject area
PSCI
Section number only
003
Section ID
PSCI1800003
Course number integer
1800
Meeting times
R 10:15 AM-11:14 AM
Meeting location
HAYD 358
Level
undergraduate
Description
Understanding and interpreting large, quantitative data sets is increasingly central in political and social science. Whether one seeks to understand political communication, international trade, inter-group conflict, or other issues, the availability of large quantities of digital data has revolutionized the study of politics. Nonetheless, most data-related courses focus on statistical estimation, rather than on the related but distinctive problems of data acquisition, management and visualization--in a term, data science. This course addresses that imbalance by focusing squarely on data science. Leaving this course, students will be able to acquire, format, analyze, and visualize various types of political data using the statistical programming language R. This course is not a statistics class, but it will increase the capacity of students to thrive in future statistics classes. While no background in statistics or political science is required, students are expected to be generally familiar with contemporary computing environments (e.g. know how to use a computer) and have a willingness to learn a variety of data science tools. You are encouraged (but certainly not required) to register for both this course and PSCI 338 at the same time, as the courses cover distinct, but complimentary material.
Course number only
1800
Fulfills
Quantitative Data Analysis
Use local description
No

PSCI1800 - Introduction to Data Science

Status
A
Activity
LEC
Section number integer
1
Title (text only)
Introduction to Data Science
Term
2023A
Subject area
PSCI
Section number only
001
Section ID
PSCI1800001
Course number integer
1800
Meeting times
MW 12:00 PM-12:59 PM
Meeting location
PCPE AUD
Level
undergraduate
Instructors
Marc Trussler
Description
Understanding and interpreting large, quantitative data sets is increasingly central in political and social science. Whether one seeks to understand political communication, international trade, inter-group conflict, or other issues, the availability of large quantities of digital data has revolutionized the study of politics. Nonetheless, most data-related courses focus on statistical estimation, rather than on the related but distinctive problems of data acquisition, management and visualization--in a term, data science. This course addresses that imbalance by focusing squarely on data science. Leaving this course, students will be able to acquire, format, analyze, and visualize various types of political data using the statistical programming language R. This course is not a statistics class, but it will increase the capacity of students to thrive in future statistics classes. While no background in statistics or political science is required, students are expected to be generally familiar with contemporary computing environments (e.g. know how to use a computer) and have a willingness to learn a variety of data science tools. You are encouraged (but certainly not required) to register for both this course and PSCI 338 at the same time, as the courses cover distinct, but complimentary material.
Course number only
1800
Fulfills
Quantitative Data Analysis
Use local description
No

PSCI1800 - Introduction to Data Science

Status
A
Activity
REC
Section number integer
2
Title (text only)
Introduction to Data Science
Term
2023A
Subject area
PSCI
Section number only
002
Section ID
PSCI1800002
Course number integer
1800
Meeting times
R 8:30 AM-9:29 AM
Meeting location
WILL 2
Level
undergraduate
Description
Understanding and interpreting large, quantitative data sets is increasingly central in political and social science. Whether one seeks to understand political communication, international trade, inter-group conflict, or other issues, the availability of large quantities of digital data has revolutionized the study of politics. Nonetheless, most data-related courses focus on statistical estimation, rather than on the related but distinctive problems of data acquisition, management and visualization--in a term, data science. This course addresses that imbalance by focusing squarely on data science. Leaving this course, students will be able to acquire, format, analyze, and visualize various types of political data using the statistical programming language R. This course is not a statistics class, but it will increase the capacity of students to thrive in future statistics classes. While no background in statistics or political science is required, students are expected to be generally familiar with contemporary computing environments (e.g. know how to use a computer) and have a willingness to learn a variety of data science tools. You are encouraged (but certainly not required) to register for both this course and PSCI 338 at the same time, as the courses cover distinct, but complimentary material.
Course number only
1800
Fulfills
Quantitative Data Analysis
Use local description
No

PSCI2200 - Preparing for Policy Work in Washington

Status
A
Activity
SEM
Section number integer
1
Title (text only)
Preparing for Policy Work in Washington
Term
2023A
Subject area
PSCI
Section number only
001
Section ID
PSCI2200001
Course number integer
2200
Level
undergraduate
Instructors
Deirdre Martinez
Description
Designed to complement a policy internship, this two credit course will focus on content and skills that are likely to be useful in typical Washington offices. Students will develop literacy on the most pressing domestic policy topics and will work on writing and presentation skills. All students will participate in a public policy internship for at least ten hours a week.
Course number only
2200
Use local description
No

PSCI7999 - Assignment to Faculty

Status
A
Activity
IND
Section number integer
4
Title (text only)
Assignment to Faculty
Term
2022C
Subject area
PSCI
Section number only
004
Section ID
PSCI7999004
Course number integer
7999
Level
graduate
Description
Assignment to faculty members for directed reading, research, or participation in a joint research project. Section number must be obtained from the Political Science office.
Course number only
7999
Use local description
No

PSCI3802 - Survey Research and Design

Status
A
Activity
LEC
Section number integer
1
Title (text only)
Survey Research and Design
Term
2022C
Subject area
PSCI
Section number only
001
Section ID
PSCI3802001
Course number integer
3802
Meeting times
MW 3:30 PM-4:59 PM
Meeting location
LRSM 112B
Level
undergraduate
Instructors
William Marble
Description
Political polls are a central feature of elections and are ubiquitously employed to understand and explain voter intentions and public opinion. This course will examine political polling by focusing on four main areas of consideration. First, what is the role of political polls in a functioning democracy? This area will explore the theoretical justifications for polling as a representation of public opinion. Second, the course will explore the business and use of political polling, including media coverage of polls, use by politicians for political strategy and messaging, and the impact polls have on elections specifically and politics more broadly. The third area will focus on the nuts and bolts of election and political polls, specifically with regard to exploring traditional questions and scales used for political measurement; the construction and considerations of likely voter models; measurement of the horserace; and samples and modes used for election polls. The course will additionally cover a fourth area of special topics, which will include exit polling, prediction markets, polling aggregation, and other topics. It is not necessary for students to have any specialized mathematical or statistical background for this course.
Course number only
3802
Use local description
No

PSCI6800 - Advanced Statistical Analysis

Status
A
Activity
REC
Section number integer
302
Title (text only)
Advanced Statistical Analysis
Term
2022C
Subject area
PSCI
Section number only
302
Section ID
PSCI6800302
Course number integer
6800
Meeting times
W 12:00 PM-12:59 PM
Meeting location
PCPE 203
Level
graduate
Instructors
Nicolas-Alberto Idrobo-Rincon
Description
The objective of this course is to provide Political Science Ph.D. students with statistical tools useful for making inferences about politics. We will cover fundamentals of probability theory, estimation, and hypothesis testing, emphasizing application to research questions in American Politics, positive Political Theory, Comparative Politics, and International Relations.
Course number only
6800
Use local description
No

PSCI0010 - Government Censorship in Authoritarian Regimes

Status
A
Activity
SEM
Section number integer
303
Title (text only)
Government Censorship in Authoritarian Regimes
Term
2022C
Subject area
PSCI
Section number only
303
Section ID
PSCI0010303
Course number integer
10
Meeting times
T 3:30 PM-6:29 PM
Meeting location
PCPE 203
Level
undergraduate
Instructors
Jane Esberg
Description
The primary goal of the first-year seminar program is to provide every first-year student with the opportunity for a direct personal encounter with a faculty member in a small class setting devoted to a significant intellectual endeavor. First-year seminars also fulfill College General Education Requirements.
Course number only
0010
Fulfills
Society Sector
Cultural Diviserity in the U.S.
Use local description
No

PSCI1801 - Statistical Methods PSCI

Status
A
Activity
REC
Section number integer
202
Title (text only)
Statistical Methods PSCI
Term
2022C
Subject area
PSCI
Section number only
202
Section ID
PSCI1801202
Course number integer
1801
Level
undergraduate
Instructors
Marc N Meredith
Description
The goal of this class is to expose students to the process by which quantitative political science research is conducted. The class will take us down three separate, but related tracks. Track one will teach some basic tools necessary to conduct quantitative political science research. Topics covered will include descriptive statistics, sampling, probability and statistical theory, and regression analysis. However, conducting empirical research requires that we actually be able to apply these tools. Thus, track two will teach us how to implement some of these basic tools using the computer program R. However, if we want to implement these tools, we also need to be able to develop hypotheses that we want to test. Thus, track three will teach some basics in research design. Topics will include independent and dependent variables, generating testable hypotheses, and issues in causalit You are encouraged to register for both this course an PSCI 107 at the same time, as the courses cover distin but complementary, material. But there are no prerequi nor is registering for PSCI 107 necessary, in order to take this course. The class satisfies the College of A Science Quantitative Data Analysis (QDA) requirement.
Course number only
1801
Fulfills
Quantitative Data Analysis
Use local description
No