Unit 1
Elementary ProgrammingData Types and Typecasting. Operators (comparison, arithmetic, and logical), variables, constants. Conditional statements. Loops, conditions, and control statements. [8 hrs]
Course Name | Programming for Data Science |
Course Code | 25SDS503 |
Program | M.Sc. in Social Data Science & Policy |
Semester | 1 |
Credits | 4 |
Campus | Faridabad |
Elementary ProgrammingData Types and Typecasting. Operators (comparison, arithmetic, and logical), variables, constants. Conditional statements. Loops, conditions, and control statements. [8 hrs]
Data Structures & functions: Data structures (e.g., lists, tuples, sets, and dictionaries). Functions. [7 hrs]
File Handling Loading CSV, JSON data; Read and Write data, Handling headers, Datatypes, Pandas, Numpy, Dataframes [10 hrs]
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]
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]
Textbooks and Papers:
Wickham, H., etinkaya-Rundel, M., & Grolemund, G. (2023). R for Data science. 2nd edition. OReilly Media.
Nelli, F. (2023). Python Data Analytics. 3rd edition. Apress Berkeley, CA.
Reference Books:
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:
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:
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 |
Assessment | Internal | External |
Programming assignments | 25 | |
Student presentations & Class participation | 20 | |
Attendance | 5 | |
End Semester | 50 |
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