Capstone Project
Advanced
30 hrs
2 Concepts
M1
Project Overview
Concept 1
End-to-End Data Pipeline
The capstone integrates all course skills: import → clean → analyse → model → visualise → report → deploy.
R
# Track A: Education Analytics
# 1. Import: read_csv('students.csv')
# 2. Clean: dplyr::filter, tidyr::drop_na
# 3. Analyse: group_by |> summarise, t.test
# 4. Model: lm() or randomForest
# 5. Visualise: ggplot2
# 6. Report: R Markdown to HTML
# 7. Deploy: Shiny dashboard on shinyapps.io
Solved Examples
Example 1
Apply the concept of End-to-End Data Pipeline to a sample dataset. Show at least two approaches.
# See the code example above and adapt it to your data. # Always check your output with str() and head().
Self-Assessment (2 questions)
Q1. What is the primary purpose of end-to-end data pipeline?
Q2. Which R package is most relevant for this topic?
M2
Deliverables
Concept 1
GitHub Repository and Shiny App
Your capstone must include: a reproducible R Markdown report, a deployed Shiny dashboard, and a clean GitHub repository.
R
# Deploy Shiny app:
library(rsconnect)
deployApp('my_app/', appName='vidaara-capstone')
# Push to GitHub:
# git init
# git add -A
# git commit -m 'Capstone: Education Analytics Dashboard'
# git remote add origin https://github.com/yourusername/capstone
# git push -u origin main
Solved Examples
Example 1
Apply the concept of GitHub Repository and Shiny App to a sample dataset. Show at least two approaches.
# See the code example above and adapt it to your data. # Always check your output with str() and head().
Self-Assessment (2 questions)
Q1. What is the primary purpose of github repository and shiny app?
Q2. Which R package is most relevant for this topic?