There’s never been a better time to get into data analytics, but if you’re looking to break into the world of data, then you may be asking yourself: what skills do I need to learn?
R is one such programming language which one should learn.R programming language was developed for statistical analysis at a small-scale in academic settings. R language is a powerful statistical computing tool for visualizing data, exploring large data sets and creating novel statistical models.R is the most popular and powerful programming language that data scientists are currently using.
With data analysis becoming more and more important in helping business's understand their customers, operational efficiency, and more, R will only become increasingly important.
Module 1: Introduction
What is R Programming?
How to download & Install R and R Studio
R Data Types, Arithmetic and Logical Operators with examples
Matrices in R
Factors in R (Categorical Variables)
Module 2: Data Preparation
R Data Frame: Create, Append, Select, Subset
R Sort a data Frame
Merge data frames in R: Full and Partial Match
Functions in R programming
Module 3: Programming
IF, IFELSE, ELSE IF statements in R
Loops in R – For, While, Do while
apply, lapply, sapply, tapply function in R
Import data into R: CSV, Excel, Text
How to replace missing values
Correlation in R
Module 4: Distributions and MLE
Discrete Distributions
Continuous Distributions
Finding the maximum likelihood estimate
Module 5: Data Analysis
Scatter Plot
Boxplot
Bar Chart and Histogram in R
Hypothesis Testing
R Anova tutorial
Module 6: Machine Learning
Simple , Multiple linear Regression in R
Generalized Linear Model in R
Decision Tree in R
K means Clustering in R
Module 7: Time Series and Forecasting
Auto Regressive (AR)
Moving Average (MA)
ARMA
ARIMA
Fitting a Model
Forecasting