Note - SPPU's Degreeplus Beta Version is up, stay tuned for more details.
Note - SPPU's Degreeplus Beta Version is up, stay tuned for more details.

Course

Data Analysis with R

50 hours 38 minutes

Credits: 3 Credits

Description

In this course, learners will develop a thorough foundation in R programming – from core language concepts and data structures to advanced statistical modelling and visualization. Learners will explore R vectors, matrices, arrays, lists, data frames, factors, and strings, and apply control flow, looping, functions, and object systems to write structured R programs. They will load, transform, filter, join, and visualize datasets, and apply a full range of statistical techniques including probability distributions, hypothesis testing, regression, classification, clustering, and ensemble modelling. Through extensive hands-on labs, learners will tackle real-world datasets covering topics such as population growth, housing, weather, life expectancy, and income prediction. They will also build advanced and interactive visualizations using R, culminating in bootcamp – style sessions that reinforce practical data visualization skills.

What Students Will Learn

  • R Programming for Beginners: Getting Started
  • R Programming for Beginners: Exploring R Vectors
  • R Programming for Beginners: Leveraging R with Matrices, Arrays, & Lists
  • R Programming for Beginners: Understanding Data Frames, Factors, & Strings
  • Final Exam: Getting Started with R Programming

  • Using R Programming Structures: Leveraging R with Control Flow & Looping
  • Using R Programming Structures: Functions & Environments
  • Using R Programming Structures: Object Systems
  • Final Exam: Applying and Using R Programming Structures

  • Datasets in R: Loading & Saving Data
  • Datasets in R: Transforming Data
  • Datasets in R: Selecting, Filtering, Ordering, & Grouping Data
  • Datasets in R: Joining & Visualizing Data
  • Final Exam: Working with Datasets in R

  • Statistical Analysis and Modeling in R: Working with Probability Distributions
  • Statistical Analysis and Modeling in R: Understanding & Interpreting Statistical Tests
  • Statistical Analysis and Modeling in R: Statistical Analysis on Your Data
  • Statistical Analysis and Modeling in R: Performing Regression Analysis
  • Statistical Analysis and Modeling in R: Performing Classification
  • Statistical Analysis and Modeling in R: Performing Clustering
  • Statistical Analysis and Modeling in R: Building Regularized Models & Ensemble Models
  • Data Analysis with R
  • Final Exam: Statistical Analysis and Modeling in R

  • Advanced and Interactive Visualization Bootcamp: Session 1 Replay
  • Advanced and Interactive Visualization Bootcamp: Session 2 Replay
  • Advanced and Interactive Visualization Bootcamp: Session 3 Replay
  • Advanced and Interactive Visualization Bootcamp: Session 4 Replay
  • Advanced Visualizations & Dashboards: Visualization Using R

  • Intro to R Lab: Population Growth
  • Data Cleaning with R Lab: US Census
  • Measuring Central Tendency with R Lab: Housing in NYC
  • Variance and Standard Deviation with R Lab: Weather in London
  • Quantiles, Quartiles, and Interquartile Range with R Lab: Life Expectancy By Country
  • Aggregating Data with R Lab: Shoefly
  • Joining Data Frames with R Lab: Page Visits Funnel
  • Linear Regression with R Lab: Predicting Income
  • Hypothesis Testing with R Lab: Blood Transfusion
  • Propensity Scores with R Lab: Cover Crops
  • R for Programmers: Mastering the Tools

Overall Learning Outcomes

  • Write and execute R programs using core data structures including vectors, matrices, arrays, lists, data frames, and strings
  • Apply control flow, looping, functions, environments, and object systems to structure R code effectively
  • Load, save, transform, filter, join, and group datasets using R
  • Visualize data in R and communicate findings clearly
  • Apply probability distributions and conduct statistical tests to draw meaningful conclusions from data
  • Perform regression, classification, and clustering analyses on real-world datasets
  • Build regularized and ensemble models to improve predictive performance
  • Conduct hands-on data analysis across diverse domains including demographics, housing, weather, and public health
  • Create advanced and interactive visualizations using R to support data-driven storytelling and dashboard development