R PROGRAMMING
OVERVIEW
R programming language start from zero knowledge, while it would help for better learning some programming or data knowledge. R programming language is considered as an easy to learng language, the R programming language is used to understand, interpret and visualize data. Nowadays companies and research institutions collect vast amount of data as well as more complex data, the R language is the chosen language for data analysis. The R language is used to do data analysis, scientific calculations and machine learning. With the right instructor training programming does not need to be difficult, programming is structured and logic. This training is 100% in practice “Hands On”. We learn by doing.
Audience
Young professional wanting to learning R programming. You could be a programmer, accounting, statistician or similar background and this course would be very interesting as well as a career advancement in the professions of data analyst, data science and machine learning.
Programming beginners and students with basic programming experience who wish to continue learning Python programming language to prepare themselves with world market knowledge. This course is a basic course and from here on you can continue to advance in data science and machine learning.
COURSE OUTLINE
At the end of the course, you will be able to build programs in the R programming language by importing data from different sources as well as processing the data with the R programming libraries, building hypotheses and statistical motifs as well as displaying the data in a visual way for presentation. Finally, you will be introduced to machine learning.
 Introduction to R
 Introduction
 Installation
 R Programming
 R Operator
 R Conditional Statement & Loop
 If Else (Conditional Statement)
 Nested If Else (Conditional Statement)
 For Loop
 While loop
 Ect
 R Operator
 R Function
 Numeric Functions (sqrt(), floor(), ect..)
 Statistical Functions (mean(), meadian(), sd() ect..)
 Creating our own Functions
 R Data Structure
 Vector
 Matrix, Array, Data Frame
 Factor
 List
 Import and Export in R

 Import CSV Data in R
 Import Text Data in R
 Import Excel, Database and Web Data in R
 Export Data – Text,
 Export Data – CSV, Excel

 Data Manipulation in R
 Apply Functions
 Apply()
 Lapply()
 Sapply()
 Tapply()
 ect
 Dplyr Package – base commands
 Dplyr Package
 Mutate
 Filter
 Arrange
 ect
 Dplyr Package –
 Summarise ()
 Pipe operator: %>%
 Group by ()
 Different Date format
 Apply Functions
 Data Visualisation

 Scatter Plot
 Line Chart
 Bar Plot
 Pie Chart
 Histogram
 Ggplot2 Package

 Introduction to Statistics
 Typo of Statistics
 Bias
 Cluster Sampling
 Systematic Sampling
 Statistics
 Quantitative and Qualitative data
 Descriptive statistics
 Distribution
 Mean
 Standard Deviation
 Formula
 Types of Distributions
 Normal distribution
 Functions in R
 Testing Hypothesis
 Null Hypothesis
 Alternative Hypothesis
 Hypothesis Test – Outcome
 Anova
 Chi Square
 Introduction to Machine Learning with R
 Machine Learning with R
 Linear Regression
 Logistic Regression
 K Nearest Neighbors
 KMeans Clustering
 Neural Networks
 Natural Language Processing
PREREQUISITES
The desire to learn R programming and data science. There is no prerequisite.