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JOHN CABOT UNIVERSITY

COURSE CODE: "PL/CS 362"
COURSE NAME: "Computational Methods for Social Science"
SEMESTER & YEAR: Spring 2024
SYLLABUS

INSTRUCTOR: Bogdan Gabriel Popescu
EMAIL: [email protected]
 HOURS: MW 10:00 AM 11:15 AM
TOTAL NO. OF CONTACT HOURS: 45
CREDITS: 3
PREREQUISITES:
OFFICE HOURS:
COURSE DESCRIPTION:
Computational social science is an interdisciplinary field that combines computer science and information technology methods with theories and concepts from the social sciences to analyze and understand social phenomena. It uses computational methods like spatial and text analysis to collect, process, and analyze datasets from various sources, such as social media, surveys, and government databases. The tools that students learn in this course have wide applicability to geography, sociology, public policy, economics, and political science. Computational social science aims to use these methods to understand social behavior and social systems better and predict future social phenomena. This course helps students develop foundational skills in spatial and text analysis and an awareness of advanced methodologies in social sciences.
SUMMARY OF COURSE CONTENT:

The course introduces students to applied computational social sciences. It aims to provide a framework in which students can implement their research ideas and it equips them with a foundational understanding of R - a free programming language for statistical computing and graphics. Lessons cover elementary programming techniques (e.g., loops, conditional statements, user-defined functions), how to generate clean code, use external libraries, develop reproducible research with R Quarto, and visualize and analyze spatial and textual data.

At the end of the course, students have the essential computational skills and awareness of relevant programming libraries to conduct their research.

Required course materials/study visits and expected expenditure for the students
Links to relevant reading materials are available here. You will download R on your computers for free.
LEARNING OUTCOMES:

Upon successful completion of this course the students will be able to:

  • execute basic programming tasks in R (e.g. loops, conditional statements, while statements, etc.)
  • analyze spatial data
  • produce maps
  • analyze text documents
  • produce text analysis reports
  • implement statistical analysis
TEXTBOOK:
NONE
REQUIRED RESERVED READING:
NONE

RECOMMENDED RESERVED READING:
NONE
GRADING POLICY
-ASSESSMENT METHODS:
AssignmentGuidelinesWeight
5 problem sets 35
Mid term exam 30
Final exam 30

-ASSESSMENT CRITERIA:
AWork of this quality directly addresses the question or problem raised and provides a coherent argument displaying an extensive knowledge of relevant information or content. This type of work demonstrates the ability to critically evaluate concepts and theory and has an element of novelty and originality. There is clear evidence of a significant amount of reading beyond that required for the course.
BThis is highly competent level of performance and directly addresses the question or problem raised.There is a demonstration of some ability to critically evaluatetheory and concepts and relate them to practice. Discussions reflect the student’s own arguments and are not simply a repetition of standard lecture andreference material. The work does not suffer from any major errors or omissions and provides evidence of reading beyond the required assignments.
CThis is an acceptable level of performance and provides answers that are clear but limited, reflecting the information offered in the lectures and reference readings.
DThis level of performances demonstrates that the student lacks a coherent grasp of the material.Important information is omitted and irrelevant points included.In effect, the student has barely done enough to persuade the instructor that s/he should not fail.
FThis work fails to show any knowledge or understanding of the issues raised in the question. Most of the material in the answer is irrelevant.
-ATTENDANCE REQUIREMENTS:
ATTENDANCE REQUIREMENTS AND EXAMINATION POLICY
You cannot make-up a major exam (midterm or final) without the permission of the Dean’s Office. The Dean’s Office will grant such permission only when the absence was caused by a serious impediment, such as a documented illness, hospitalization or death in the immediate family (in which you must attend the funeral) or other situations of similar gravity. Absences due to other meaningful conflicts, such as job interviews, family celebrations, travel difficulties, student misunderstandings or personal convenience, will not be excused. Students who will be absent from a major exam must notify the Dean’s Office prior to that exam. Absences from class due to the observance of a religious holiday will normally be excused. Individual students who will have to miss class to observe a religious holiday should notify the instructor by the end of the Add/Drop period to make prior arrangements for making up any work that will be missed. The final exam period runs until ____________
ACADEMIC HONESTY
As stated in the university catalog, any student who commits an act of academic dishonesty will receive a failing grade on the work in which the dishonesty occurred. In addition, acts of academic dishonesty, irrespective of the weight of the assignment, may result in the student receiving a failing grade in the course. Instances of academic dishonesty will be reported to the Dean of Academic Affairs. A student who is reported twice for academic dishonesty is subject to summary dismissal from the University. In such a case, the Academic Council will then make a recommendation to the President, who will make the final decision.
STUDENTS WITH LEARNING OR OTHER DISABILITIES
John Cabot University does not discriminate on the basis of disability or handicap. Students with approved accommodations must inform their professors at the beginning of the term. Please see the website for the complete policy.
SCHEDULE
Week 1: Intro to Computational Social Science
Week 2: Data Wrangling
Week 3: Reading Data
Week 4: Functions and Pipes
Week 5: Visualization and the Syntax of Graphics
Week 6: Spatial Data 1
Week 7: Spatial Data 2
Week 8: Spatial Data 3
Week 9: Fundamentals of Text Analysis
Week 10: Intermediate Text Analysis
Week 11: Applied Text Analysis
Week 12: Basic Statistics
Week 13: Intermediate Statistics
Week 14: Applications