This course aims to teach learners how to build end-to-end data science projects using the R programming language. The learning outcomes include mastering exploratory data analysis, data visualization, machine learning model building and deployment, data pre-processing, and web application development using Shiny in R. The course covers skills such as handling missing data, building classification and linear regression models, deploying web applications, and creating interactive data-driven web applications. The teaching method involves practical tutorial videos that provide step-by-step guidance. This course is intended for individuals interested in advancing their skills in R programming and applying them to real-world 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
