SemesterSpring Semester, 2025
DepartmentProgram(undergraduate level)
Course NameData Science Fundamentals for the Social Sciences
InstructorReidhead Jacob
Credit3.0
Course TypeElective
Prerequisite
Course Objective
Course Description
Course Schedule









































































































Week Topic Content and Reading Assignment Teaching Activities and Homework
1 Role of Data Science in Social Science Research  

 


2 Coding I data types, control structures Coding task
3 Coding II functions, classes Coding task
4 File Management & I/O os, dir, paths, file types: xlsx, csv, json, xml, txt Coding task
5 Data Collection online APIs, web-scraping Coding task
6 Wrangling & Curation I selection queries, simple covariates Coding task &

Datathon I: Collect raw data files from Internet
7 Wrangling & Curation II grouping, aggregate fxns and agg covariates Coding task
8 Wrangling & Curation III joins, melt & pivot, indexes & look-up tables Coding task
9 Data Cleaning recoding, missing values, errors, disambig. Coding task
10 Visualization I univariate & bivariate graphs Coding task &

Datathon II: Produce Clean, Data Table from Raw Data
11 Visualization II multivariate graphs Coding task
12 Visualization III dynamic graphs, dashboards Coding task
13 Analysis I descriptive statistics, t-tests of means & props Coding task &

Datathon III: Produce Graphs from Clean Data
14 Analysis II clustering, unsupervised labels as covariates Coding task
15 Analysis III linear and logistic regression Coding task
16 Data Dashboard Presentation present data dashboard Datathon IV: Describe and Model Clean Data

Teaching Methods
Teaching Assistant

To be determined


Requirement/Grading




























Task Points per Assessment Percent of Semester Grade
Attendance After TWO FREE ABSENCES, each unexcused absence is -5% 0%
Tasks 10 tasks * 2% each 20%
Datathons 4 challenges * 10% each 40%
Data Dashboard Presentation 1 data dashboard & presentation 40%


 


Textbook & Reference

Molin, S. (2021). Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization. Packt Publishing Ltd.


Urls about Course
Moodle & Google Drive Links – To Be Announced
Attachment

Syllabus _ Data Science Fundamentals.pdf