Course

Unit I

Text as data – Loading text data. Cleaning. Tokenization, Stemming, Stopword removal. Word cloud visualization. Tools for advanced visualization. Temporal analysis on text data. 6 hrs.

Unit II

Tools for Multi modal data – Text to Audio. Audio to Text. Tools for text translations. 6 hrs.

Unit III

Data Models – Data models (Taxonomies, Ontologies, Meta-data schema. Entities and Relationships. 6 hrs.

Unit IV

Data storage – Types of Data storage and storage methods. Data model representations (ER diagrams, Data flow diagrams). 6 hrs.

Unit V

Object Oriented Programming – Concepts of OOP. Class members and function (Encapsulation). 6 hrs.

Prerequisite: Programming for Social Data Science – I

Summary: This course is a continuation of Programming for Social Data Science I and focuses on programming tools for working with non-numerical data, such as text and audio. Complementary to the “deductive” approach of testing hypotheses using quantitative data in PSDS 1, in PSDS 2 students familiarize themselves with “inductive” approaches of collecting and analyzing qualitative data to shape theories and hypotheses. Students learn how to read and understand qualitative code, as well as to write and debug their own code. Essential concepts like conversation analysis, metaphor analysis, domain analysis, membership categorization analysis, visual data and discourse analysis are also introduced. Students learn to use R and Taguette software for qualitative analyses. R and Taguette are popular qualitative research tools that allow for free, open-source, replicable analyses. More advanced qualitative options available in commercial software such as ATLAS.ti and Nvivo are also introduced. The objectives of this course are the same as Programming for Social Data Science I.

Course Objectives:

1. Understanding the approaches to utilizing qualitative data for shaping social science theories and hypotheses
2. Understand the application of qualitative programming in Social Data Science
3. Define and understand basic procedures for the preparation, cleaning, and analyzing of qualitative data
4. Implement and use functions and operate on qualitative files to read

Course Outcomes:

• CO1: Apply basic programming skills to investigate qualitative data, interpret statistical output, and generate novel hypotheses.
• CO2: Identify optimal statistical approaches for analyzing social problems based on qualitative data characteristics and assumptions, including content type, sample characteristics, and noise reduction.
• CO3: Construct reproducible code with a theoretical and statistical justification for the decision-making process.
• CO4: Present a structured argument for fine-tuning policies in the social domain, based on empirical qualitative output.

Skills:

• Structured thinking: students will learn to structure sets of qualitative data concerning a social problem in an empirical, reproducible way, that allows for a reliable conclusion.
• Scientific communication: students will enhance their ability to summarize large quantities of written or auditive data, incorporating broader patterns as well as specific exemplary excerpts in order to communicate meaningful conclusions.

Program Specific Outcomes PSO – Course Objectives – Mapping

Program outcome PO – Course Outcomes CO Mapping

Evaluation Pattern:

*CA – Can be Quizzes, Assignment, Projects, and Reports, and Seminar

1. Estrada, S. (2017). Qualitative Analysis Using R: A Free Analytic Tool. The Qualitative Report, 22(4), 956-968. https://doi.org/10.46743/2160-3715/2017.2659
2. Rampin, R. & Rampin, V. (2021). Taguette: Open-source qualitative data analysis. Journal of Open Source Software. https://joss.theoj.org/papers/10.21105/joss.03522

1. Dauber, R. (2024). R for Non-Programmers: A Guide for Social Scientists. Mixed-methods research: Analysing qualitative data in R. https://bookdown.org/daniel_dauber_io/r4np_book/mixed-methods-research.html
2. Thiem, A. & Duşa, A. (2012). Qualitative Comparative Analysis with R: A User’s Guide. Springer.
3. Temple University’s Qualitative Data Analysis and QDA Tools. Taguette Guide. https://guides.temple.edu/qda/taguette
4. NYU Libraries for Qualitative Data Analysis: Taguette Guide. https://guides.nyu.edu/QDA/Taguette