Capstone Project
Advanced
30 hrs
2 Concepts
Your Learning Map
📌 You already know
You have learned to import, wrangle, visualise, model and report.
🎯 You'll learn here
Tying it all together in one end-to-end pipeline, version-controlled on GitHub and delivered as a Shiny app.
🌍 Where it's used
This is the portfolio piece employers and admissions actually look at.
🔗 Unlocks next
Sets up the career chapter — turning skills into roles.
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
R — Load to summarise to report
LIVE READY
Output (verified)
cyl mpg 1 4 26.7 2 6 19.7 3 8 15.1
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. A typical data-analysis pipeline runs in the order:
You import data, clean and wrangle it, analyse/visualise, then report the findings.
Q2. Making an analysis 'reproducible' mainly means:
Reproducibility means the same code and data reliably produce the same outputs for others.
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. Git and GitHub are used in a project to:
Git records versions of your code; GitHub hosts it for sharing and collaboration.
Q2. Deploying a Shiny app lets users:
A deployed Shiny app gives non-R users an interactive web interface to your analysis.