Semester	Spring Semester, 2025
Department	The International Master Program of Applied Economics and Social Development (IMES) , First Year The International Master Program of Applied Economics and Social Development (IMES) , Second Year
Course Name	Data Science Fundamentals for the Social Sciences
Instructor	Reidhead Jacob
Credit	3.0
Course Type	Elective
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