Course

Unit I

Unit I – Introduction to Programming – What is Programming?; What Could it Mean for Social Science Research? Concept of automation. Programming languages. Applications in Social Science.

Unit II

Unit II – Data wrangling and Descriptive analysis – Installations and Setting up the programming environment. Identifying and loading datasets. Data Wrangling, Filter, select, Apply, order, sort. Grouping and Summarizing Data. Tidying datasets (e.g., data cleaning techniques and handling missing data).

Unit III

Unit III – Data visualization using gg-plot or Matplotlib – Ethics considerations. Data manipulations and
Exploratory analysis. Graphs, plots, configurations. Histograms, Bar Plots, Scatterplots, Combining Multiple Plots, Saving Plots. Interactive visualizations. Visualizing geographical data.

Unit IV

Unit IV – Elementary programming – Data types and Typecasting. Operators (comparison, arithmetic and
logical), variables, constants. Conditional statements. Loops , conditions and control statements. Data structures (e.g., List, tuples, sets and dictionaries). Functions.

Unit V

Unit V – Exploratory analyses – Familiarize with main packages like Numpy, Pandas, matplotlib, etc. Numeric
exploration and visual exploration. Correlations and heatmaps. Patterns and shapes. Plotting distributions and related statistics.

Programming for Social Data Science is a gentle introduction to programming concepts that are paramount to data science in general, and to social data science in particular. Students learn how to read and understand existing code, as well as to write and debug their own code. Basic computing algorithms are introduced, implemented, and their computational cost is being assessed. Essential programming concepts like object-oriented programming, and primitive and compound data types are also introduced. Students learn the R and Python programming language, which have grown to become the most popular among social scientists for numerous good reasons.

The focus of the course is on analyzing data and generating reproducible research through the use of the programming language R and version control software. Topics include coding concepts (e.g., data structures, control structures, functions, etc.), data visualization, data wrangling and cleaning, exploratory data analysis, etc. Major emphasis is placed on a pragmatic understanding of core principles of programming and packaged implementations of methods.

Course Objectives:

• Understanding about the approaches to solving Social Problems with Data
• Understand the application of programming in Social Data Science
• Define and understand variables and use sets, loops and conditional statements
• Implement and use functions and operate on files to read

Course Outcomes:

CO1: Apply basic programming skills to investigate social problems and interpret statistical output.
CO2: Identify optimal statistical approaches for analyzing social problems based on data characteristics and assumptions, including measurement levels, data distribution, and dataset structure.
CO3: Construct reproducible code with a theoretical and statistical justification for the decision-making process.
CO4: Present a structured argument for government intervention in the social domain, based on a nuanced and critical understanding of statistical findings.

Skills:

• Structured thinking: students will learn to structure their thinking to approach social problems from a data science perspective, and take organized steps towards a conclusion.
• Scientific communication: students will enhance their ability for verbal and written communication of statistical output as well as its interpretation and broader implications.

Program outcome PO – Course Outcomes CO Mapping

Program Specific Outcomes PSO – Course Objectives – Mapping