50,000+ Free Courses Online to Start Today

Coursesity is supported by learner community. We may earn affiliate commission when you make purchase via links on Coursesity.

Data Analysis with R

Data is everywhere and so much of it is unexplored. Learn how to investigate and summarize data sets using R and eventually create your own analysis.

In this course you will learn :

Begin by learning about exploratory data analysis (EDA) and why it is so important.
Visual methods are used to analyse and summarise data sets in EDA, which comes before formal hypothesis testing and modelling.
R will be our tool for creating visuals and performing analyses.
Installing RStudio and packages, learning the layout and basic commands of R, practising writing basic R scripts, and inspecting data sets will all be covered.
EDA is used to understand a variable's distribution and to look for anomalies and outliers.
To make sense of a pseudo-data set of Facebook users, learn how to quantify and visualise individual variables within a data set.
In visualisations, create histograms and boxplots, transform variables, and investigate tradeoffs.
Before building predictive models, we can use DA to identify the most important variables and relationships in a data set.
Learn how to investigate the relationship between any two variables in a data set.
Make scatter plots, compute correlations, and look into conditional means.
Discover effective methods and visualisations for examining relationships between multiple variables.
How to reshape data frames and use aesthetics such as colour and shape to uncover more information
Continue to develop intuition around the Facebook data set while also exploring some new data sets.
Examine the diamonds data set with Facebook Data Scientist Solomon Messing.
See how predictive modelling can help us determine an appropriate price for a diamond.