In this module of series on Data Science and Machine Learning with R, you will learn about data manipulation & statistics basics, merging, data creation, merge example, what is statistics, variables, quantiles, calculating variance, calculating covariance, cumulative frequency, statistics, probability & distribution etc
Data Science and Machine Learning with R (Part #2) – Statistics with R
In the recent years R has become the widely used programming language for computational statistics, visualization and data science. R is used by many statisticians, scientists and data analysts to find a solution for their problem in various fields. R is used as the most important tool for business analytics in companies like Google, Facebook and LinkedIn. R contains every data analytics techniques at your fingertips. It helps you to perform some of the most commonly performed tasks by business analysts with the help of its 4000 packages.
Through this course we will learn about data manipulation & statistics basics, merging, data creation, merge example, what is statistics, variables, quantiles, calculating variance, calculating covariance, cumulative frequency, library (mass), head (faithful), scatter plot, control flow, statistics, probability & distribution, random variable & examples, discrete example, practice problem, continuous case, exponential distribution practice problem, expected value, gambling example, deal or no deal, distribution details, expected value from binomial, uniform random variables and probability distributions examples.
This course will be useful for students and Data analysts and people from Analytics domain. It will also be useful for people who wanted to learn business analytics using R.
The pre requisites for this course include basic knowledge of statistics. Knowledge in any other programming language is an added advantage but not a must.