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 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
  •      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
    • K-Means Clustering
    • Neural Networks
    • Natural Language Processing

PREREQUISITES

The desire to learn R programming and data science. There is no prerequisite.

PURCHASE