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JOHN CABOT UNIVERSITY
COURSE CODE: "PL/CS 362"
COURSE NAME: "Computational Methods for Social Science"
SEMESTER & YEAR:
Spring 2025
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SYLLABUS
INSTRUCTOR:
Bogdan Gabriel Popescu
EMAIL: bogdan.popescu@johncabot.edu
HOURS:
MW 10:00 AM 11:15 AM
TOTAL NO. OF CONTACT HOURS:
45
CREDITS:
3
PREREQUISITES:
OFFICE HOURS:
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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.
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SUMMARY OF COURSE CONTENT:
This is an introductory course to text-as-data in Python, a free programming language and environment developed for statistical computing and graphics. The first part of the course will introduce you to the python programming language. The second part will focus on the Python’s ability to analyze text data: including social media posts, email correspondence, customer reviews, presidential debates. Through hands-on exercises, this course introduces students to the concepts and applications of text analysis. The full syllabus is available at: https://bgpopescu.net/files/teaching/text_analysis/text_analysis_syllabus.html
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LEARNING OUTCOMES:
Upon successful completion of this course the students will be able to:
- execute basic programming tasks in python (e.g. loops, conditional statements, while statements, etc.)
- understand basic text analysis terms and concepts
- utilize python for conducting text analysis.
- create a professional website containing a portfolio
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TEXTBOOK:
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REQUIRED RESERVED READING:
RECOMMENDED RESERVED READING:
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GRADING POLICY
-ASSESSMENT METHODS:
| Assignment | Guidelines | Weight |
| 4 problem sets | | 50 |
| final presentation | | 25 |
| final project report | | 25 |
-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:
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.
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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.
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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.
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SCHEDULE
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Week 1: Intro to Python
Week 2: Variables, Conditional Statements, Lists, and Loops
Week 3: Operations with Lists, Tuples, Functions
Week 4: Dictionaries and Sets
Week 5: Pandas
Week 6: Data Merging and Visualization
Week 7: Text Analysis Intro
Week 8: NLP and Sentimental Analsysis
Week 9: Topic Modeling
Week 10: Text Classification
Week 11: Ethical Condideration
Week 12: Project Development
Week 13: Course Reflection
Week 14: Student Presentations
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