Course

Unit 1

Introduction to Data Science (8 Hrs.)

Data science pipeline and components; Data science Methodologies; Concepts of Machine Learning; Principles of research design; Supervised and unsupervised learning; Biases and variances in the data; Class imbalances;

Unit 2

Text as Data (8 Hrs.)

Text cleaning techniques, Text annotation, Keyword analysis, Sentiment analysis, Taex classification, and summarization; NLP tools; Visualization of text data

Unit 3

Predictive modeling in social science (8 Hrs.)

Data cleaning techniques. Data transformation; Linear Regression; Logistic Regression; Decision Trees; Classification and categorization techniques;

Unit 4

Advanced tools for data collection and analysis ( 6 Hrs.)

Machine learning Tools (WEKA); Web Scraping; Text Analytics tools without coding (MonkeyLearn)

Text Books / References

Textbooks

1. Cioffi-Revilla, Claudio. Introduction to computational social science. Springer London. https://doi. org/10.1007/978-1-4471-5661-1, 2014.
2. Michael Crawley, The R Book, Second Edition, https://www.cs.upc.edu/~robert/teaching/estadistica/TheRBook.pdf  
3. Larson-Hall, Jenifer. A guide to doing statistics in second language research using SPSS and R. Routledge, 2015. https://www.mdthinducollege.org/ebooks/statistics/A_Guide_to_Doing_Statistics_in_Secon d_Language_Research_Using_SPSS.pdf 

References:

1. Introduction to Social Data Science by David Garcia, 2021 https://dgarciahttps://dgarcia-eu.github.io/SocialDataScience/1_Introduction/011_IntroductionToSDS/Introduction.htmleu.github.io/SocialDataScience/1_Introduction/011_IntroductionToSDS/Introduction.html 
2. Mariani, Paolo, and Mariangela Zenga, eds. Data Science and Social Research II: Methods, Technologies and Applications. Springer Nature, 2020.

Course Objectives:

1. Introduce students to the fundamental concepts and principles of data science.
2. Provide a foundation in programming languages relevant to social data science (python or R)
3. Provide an introduction to basic NLP techniques for analysing text data
4. Familiarize students with various analysis tools that integrate computational methods into data analysis.

Course Outcomes:

• CO1: Understands the significance of computational approach in social science research
• CO2: Basic programming skills with Python/R
• CO3: Encourage critical thinking in the application of computational methods to social issues.
• CO4: Gains hands-on experience in leveraging basic machine learning and NLP techniques.
• CO5: Understanding fundamental concepts of machine learning, including supervised and unsupervised learning.

Skills:

• Basic programming skills with Python/R.
• Use advanced tools to draw indepth analytical inferences.
• Proficiency in designing research studies by selecting appropriate computational approaches to address the social problems.
• Familiarity with natural language processing (NLP) tools for analyzing and extracting insights from text data.
• Capability to apply data science and machine learning techniques to solve real-world problems.

CO-PO Mapping