Introduction to Data Science with R
- Life Time Access
- Certificate on Completion
- Access on Android and iOS App
This course introduces R programming environment as a way to have hands-on experience with Data Science. It starts with a few basic examples in R before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.
This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming. The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programs.
- Writing simple R programs to do basic mathematical and logical operations.
- Loading structured data in a R environment for processing.
- Creating descriptive statistics and visualizations.
- Finding correlations among numerical variables.
- Using regression analysis to predict the value of a continuous variable.
- Building classification models to organize data into pre-determined classes.
- Organizing given data into meaningful clusters.
- Applying basic machine learning techniques for solving various data problems.
This video introduces R with some basic commands and code, then it gets into data loading, processing, and correlation analysis. Make sure to have R and RStudio installed before starting this video. [26:24]
In this video segment, we will see how to use R for solving a classification problem. We will use the wine dataset and kNN classifier. [18:02]
In this video segment, we will see how to use R to address the problems where the data doesn't have clear, desired class labels. Instead, we are interested in somehow organizing the data. [14:19]
From 0 to 1: Hive for Processing Big DataLoony Corn