This repository contains coursework, practical exercises, and projects completed for the Programming with R and Python university course. The course focuses on using Python and R for data analysis, visualization, numerical computing, reproducible research, and general problem-solving.
The repository demonstrates practical experience with both languages through Jupyter notebooks, Quarto documents, data-wrangling exercises, visualization tasks, image-based analysis, and small programming projects.
The course covers core programming and data-science concepts including:
- Python and R programming for data analysis
- Numerical computing and array operations
- Data cleaning, transformation, grouping, joining, and reshaping
- Data visualization
- Functional programming in R
- Regular expressions and text manipulation
- Image and array processing
- Algorithmic problem-solving and program development
- Reproducible analysis using Jupyter, Quarto, and
renv
Python/numpy-VaniPant/
A collection of exercises focused on NumPy and numerical array programming.
The notebook includes work with:
- NumPy arrays
- Vectorized operations
- Array manipulation
- Numerical data processing
- Basic image representation and manipulation using arrays
Sample image files, including an MNIST digit image, are used to demonstrate how image data can be represented and processed numerically.
Python/python-minesweeper-VaniPant/
A Python implementation of the Minesweeper game, developed as a programming exercise.
The project demonstrates:
- Program logic and control flow
- Grid-based algorithms
- Game-state management
- Conditional logic
- Iterative development and refinement
Multiple notebook versions document the development of the implementation.
Python/coco/
A notebook-based project working with a collection of COCO-style image data.
The project includes:
- Loading and working with image datasets
- Image inspection and processing
- Batch-oriented image operations
- Notebook-based exploratory workflows
The associated image collection is stored alongside the notebook to support the analysis.
RFiles/ggplot-penguins-VaniPant/
A Quarto-based practical focused on data visualization using the grammar of graphics approach.
Topics include:
- Aesthetic mappings
- Plot layers
- Faceting
- Visual encoding
- Plot customization
- Exploratory data visualization
The project also uses renv to manage and reproduce the R package environment.
RFiles/wrangling-and-grouping-VaniPant/
Exercises covering common data transformation workflows such as:
- Filtering and selecting data
- Grouping observations
- Aggregating data
- Creating derived variables
- Summarizing datasets
RFiles/join-and-pivot-VaniPant/
Exercises focused on combining and restructuring datasets, including:
- Table joins
- Wide-to-long transformations
- Long-to-wide transformations
- Relational data operations
RFiles/functional-programming-VaniPant/
A Quarto practical introducing functional programming techniques in R.
The exercises focus on:
- Writing reusable functions
- Applying functions across data structures
- Mapping operations
- Functional programming patterns
RFiles/regular-expressions-VaniPant/
Course exercises covering pattern matching and text-processing techniques using regular expressions.
RFiles/lego-project-VaniPant/
An additional R-based course project applying data manipulation and analysis techniques to a structured dataset.
The coursework in this repository demonstrates practical experience with:
Python
Python · NumPy · Jupyter Notebook · Array Programming · Image Processing · Algorithmic Problem Solving
R
R · Quarto · ggplot · Data Wrangling · Grouping · Joins · Pivoting · Regular Expressions · Functional Programming
Data Science Practices
Exploratory Data Analysis · Data Visualization · Numerical Computing · Reproducible Analysis · Notebook-Based Development
This repository documents the progression from fundamental programming concepts to practical data-analysis workflows in both Python and R. It demonstrates the use of each language for numerical computing, data manipulation, visualization, reproducible analysis, and structured problem-solving through hands-on coursework and projects.