Back to Catalog

This item is coming soon to the catalog. Stay tuned to see it come live!

Capstone Project: Data Science with R

IntermediateCourse

This capstone course applies core data science skills to real-world datasets through hands-on analysis and modeling with tools like R, SQL, Tidyverse, and visualization tools, culminating in an interactive dashboard and executive presentation.

Coming Soon

Language

  • English

Topic

  • R Programming

Skills You Will Learn

  • Dashboards, Data Science, R (Programming Language), SQL, Statistical Analysis, Visualization Tools

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 24 Hours

Platform

  • SkillsNetwork

Last Update

  • February 10, 2026
About this Course
This capstone course provides a comprehensive, project-based experience that brings together the data science skills and techniques developed throughout the IBM Data Analytics with Excel and R Professional Certificate. You will assume the role of a newly hired Data Scientist and work through a realistic business challenge requiring end-to-end data analysis on real-world datasets.

After completing this capstone project, you will be able to:

  • Collect data from multiple sources, including writing web scraping programs to extract HTML data and convert it into structured data frames
  • Prepare datasets for modeling by handling missing values, normalizing and formatting data, binning variables, and encoding categorical features
  • Perform exploratory data analysis using SQL, Tidyverse, and ggplot2 to generate descriptive statistics, correlations, and visual insights
  • Apply linear regression models to analyze relationships in real-world datasets and interpret analytical results
  • Create effective visualizations and build an interactive Shiny dashboard with Leaflet maps to communicate findings
  • Present complete data analysis reports, including an executive summary, tailored to technical and non-technical stakeholders
 
The course begins by having you collect and understand data from multiple sources. This includes writing a web scraping program to extract data from HTML pages. Using Tidyverse, you will perform data wrangling and preparation tasks such as handling missing values, formatting and normalizing data, binning variables, and converting categorical values into numeric form. 
 
Exploratory data analysis will be conducted using SQL, Tidyverse, and ggplot2, enabling you to generate descriptive statistics, correlations, and visual insights.
 
As part of the modeling phase, you will apply linear regression to analyze relationships within the data and create charts and plots to communicate findings effectively. You will also design and build an interactive dashboard and R Shiny application incorporating a Leaflet map to support stakeholder exploration of results.
 
The course culminates in a formal data analysis presentation, including an executive summary tailored to organizational stakeholders. Successful completion demonstrates your readiness to perform applied data science tasks, from data acquisition and preparation through modeling, visualization, and professional communication.

The following skills are required to be successful with this course: 
  • We recommend completing all previous courses in the IBM Data Analytics with Excel and R Professional Certificate before starting this capstone.

Course Syllabus


Module 1 - Capstone Overview and Data Collection

  • Capstone Overview
  • Data Collection Overview
  • Hands-on Lab: Complete the Data Collection with Web Scraping Notebook
  • Hands-on Lab: Complete the Data Collection with OpenWeather API Notebook
  • Graded Checkpoints

Module 2 - Data Wrangling 

  • Module Introduction and Learning Objectives
  • Data Wrangling Overview
  • Hands-on Lab: Complete Data Wrangling with Regular Expressions Notebook
  • Hands-on Lab: Complete Data wrangling with dplyr Notebook
  • Graded Checkpoints

Module 3 - Performing Exploratory Data Analysis with SQL, Tidyverse & ggplot2 

  • Module Introduction and Learning Objectives
  • Hands-on Lab: Load Data into Db2 on IBM Cloud
  • Hands-on Lab: Complete the EDA with SQL Lab
  • Hands-on Lab: Complete the EDA with Data Visualization Lab
  • Graded Checkpoints

Module 4 - Building a Shiny Dashboard 

  • Module Introduction and Learning Objectives
  • Predict Bike-Sharing Demand Using Regression Models
  • Hands-on Lab: Complete the Building a Baseline Regression Model Lab
  • Hands-on Lab: Complete the Improving the Linear Model lab
  • Graded Checkpoints

Module 5 - Building a R Shiny Dashboard App 

  • Module Introduction and Learning Objectives
  • Create a R Shiny Dashboard
  • Hands-on Lab: Build a Bike-Sharing Demand Prediction App with R Shiny and Leaflet
  • Hands-on Lab: Enhance the Bike-Sharing Demand Prediction App with City Details Plots
  • Graded Checkpoints

Module 6 - Present Your Data-Driven Insights

  • Module Introduction and Learning Objectives
  • Elements Of A Successful Data Findings Report
  • Structure Of A Report
  • Best Practices For Presenting Your Findings
  • (Optional) Hands-on Lab: Getting Started With PowerPoint For The Web
  • (Optional) Hands-on Lab: Basics of PowerPoint
  • (Optional) Hands-on Lab: Save your PowerPoint Presentation as PDF
  • Final Submission Overview and Instructions
  • Exercise: Preparing Your Presentation (with provided slide template)
  • Peer Review: Submit your Work and Review your Peers