Embark on a comprehensive journey through data science using R in this 3.5-hour tutorial series. Learn to perform exploratory data analysis, create impactful visualizations, build and deploy machine learning models, handle missing data, and develop interactive web applications with Shiny. Master techniques like classification, linear regression, and parallel computing to enhance your data science skills. Apply your knowledge to real-world scenarios, including health monitoring applications and data-driven web tools. Gain practical experience in deploying R Shiny apps to Heroku and efficiently managing missing data, equipping you with essential skills for end-to-end data science projects.
Overview
Syllabus
Exploratory Data Analysis in R: Towards Data Understanding.
Exploratory Data Analysis in R: Quick Dive into Data Visualization.
Machine Learning in R: Building a Classification Model.
Machine Learning in R: Repurpose Machine Learning Code for New Data.
Machine Learning in R: Deploy Machine Learning Model using RDS.
Data Pre-processing in R: Handling Missing Data.
Machine Learning in R: Speed up Model Building with Parallel Computing.
Web Apps in R: Building your First Web Application in R | Shiny Tutorial Ep 1.
Web Apps in R: Build Interactive Histogram Web Application in R | Shiny Tutorial Ep 2.
Web Apps in R: Building Data-Driven Web Application in R | Shiny Tutorial Ep 3.
Web Apps in R: Building the Machine Learning Web Application in R | Shiny Tutorial Ep 4.
Web Apps in R: Build BMI Calculator web application in R for health monitoring | Shiny Tutorial Ep 5.
Making Scatter Plots in R [Data Visualisation in R series].
Machine Learning in R: Building a Linear Regression Model.
Web Apps in R: How to Deploy R Shiny web app to Heroku | Shiny Tutorial Ep 6.
How to handle missing data in R (Ft. @Statistics Globe).
Taught by
Data Professor
