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Course Detail

Course Name Programming for Data Science
Course Code 25SDS503
Program M.Sc. in Social Data Science & Policy
Semester 1
Credits 4
Campus Faridabad

Syllabus

Unit 1

Elementary ProgrammingData Types and Typecasting. Operators (comparison, arithmetic, and logical), variables, constants. Conditional statements. Loops, conditions, and control statements. [8 hrs]

Unit 2

Data Structures & functions: Data structures (e.g., lists, tuples, sets, and dictionaries). Functions. [7 hrs]

Unit 3

File Handling Loading CSV, JSON data; Read and Write data, Handling headers, Datatypes, Pandas, Numpy, Dataframes [10 hrs]

Unit 4

Data wrangling and descriptive analysisFilter, select, apply, order, sort. Data manipulations grouping and summarizing data. Tidying datasets (e.g., data cleaning techniques and handling missing data). [10 hrs]

Unit 5

Data visualization Ethics considerations. using ggplot or Matplotlib. Graphs, plots, configurations. Histograms, bar plots, scatterplots, and interactive visualizations. Visualizing geographical data. Visual integrity (Tuftes principles), labeling and clarity [15 hrs]

Text Books / References

Textbooks and Papers:

Wickham, H., etinkaya-Rundel, M., & Grolemund, G. (2023). R for Data science. 2nd edition. OReilly Media.

https://r4ds.hadley.nz/

Nelli, F. (2023). Python Data Analytics. 3rd edition. Apress Berkeley, CA.

Reference Books:

  1. Introduction to R for Social Scientists – A Tidy Programming Approach https://www.routledge.com/Introduction- to-R-for-Social-Scientists-A-Tidy-Programming-Approach/Kennedy-Waggoner/p/book/9780367460723
  2. Python for Social Scientists https://gawron.sdsu.edu/python_for_ss/
  3. Core Python Programming https://www.udemy.com/course/core-python-3-and-oop-course-for-absolute- beginners/

Introduction

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.

Objectives and Outcomes

Course Objectives:

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

Course Outcomes:

CO1: Develop an understanding of fundamental programming constructs such as data types, variables, operators, conditional statements, loops, and user-defined functions using a high-level programming language (Python).

CO2: Build practical skills in file handling techniques for reading, writing, and managing structured data formats (e.g., CSV, JSON), and working with tabular data using libraries like pandas and numpy.

CO3: Apply data wrangling operations such as filtering, grouping, summarizing, and cleaning to prepare real-world datasets for exploratory analysis and policy research.

CO4: Create clear, ethical, and visually effective data visualizations using libraries such as matplotlib or ggplot, including static and interactive visual representations of numerical and geographic data.

CO5: Interpret basic descriptive statistics and visual summaries to communicate insights drawn from raw data, particularly in social and policy contexts.

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

PO 1 PO 2 PO 3 PO 4 PO 5 PO 6 PO 7 PO8
CO1 X
CO2 X
CO3 X
CO4 X
CO5 X

Program Specific Outcomes PSO – Course Objectives – Mapping

PSO1 PSO2 PSO3 PSO4 PSO5
CO1 X
CO2 X
CO3 X
CO4 X

Evaluation Pattern

Assessment Internal External
Programming assignments 25
Student presentations & Class participation 20
Attendance 5
End Semester 50

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