Artificial Intelligence

Foundation R Training Course CPLR 701

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Classroom Training Program: Foundation R Training CPLR 701

The objective of the course is to enable participants to gain a mastery of the fundamentals of R and how to work with data.

This Course is designed to be taught in Crash Course format.

Duration 2 Days /12 hours.

Offered M-F 8:00AM-2:30 PM, Sat & Sun: 9:00AM-3:30 PM

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

Course Outline:

Basic overview of R and R Studio:

  • R overview
  • R Studio Environment Windows
    • Script Editor Window
    • Data Environment
    • Console
    • Plots/Help/Packages

Working with Data:

  • Introduction to vectors and matrices (data.frame)
  • Different types of variables
    • Numeric, Integer, factor etc
    • Changing variable types
    • Importing data using R Studio menu functions
    • Removing variables ls() command
  • Creating variables at the console prompt – single, vector, data frame
  • Naming vectors and matrices
  • Head and tail commands
  • Introduction to dim, length and class
  • Command line import (reading .csv and tab delimited .txt files)
  • Attaching and detaching data (advantages vs data.frame$)
  • Merging data using cbind and rbind.

Exploratory Data Analysis

  • Summarising data
  • Summary command on both vectors and data frames
  • Sub-setting data using square brackets
    • summarising and creating new variables
  • Table and summary commands
  • Summary statistic commands
    • Mean
    • Median
    • Standard Deviation
    • Variance
    • Count & frequencies
    • Min & Max,
    • Quartiles
    • Percentiles
    • Correlation

Exporting data

    • Write table .txt
    • Write to a .csv file

R Workspace

  • Concept of Working Directories and Projects (menu driven and code – setwd())

Introduction to R scripts

  • Creating R Scripts
  • Saving scripts
  • Workspace images

Concepts of packages

  • Installing packages
  • Loading packages into memory

Plotting data (using standard default R plot command and ggplot2 package)

  • Bar Charts and Histograms
  • Boxplots
  • Line charts / time series
  • Scatter plots
  • Stem and leaf
  • Mosaic
  • Modifying plots
    • Titles
    • Legends
    • Axis
    • Plot Area

Course Features

  • Students 7 students
  • Max Students25
  • Duration12 hour
  • Skill levelall
  • LanguageEnglish
  • Re-take courseN/A
Curriculum is empty

Instructor

Rishabh Rawat

Data Scientist with around 5 years of experience executing data-driven solutions using python to increase efficiency, accuracy, and utility of internal data processing. Experienced at creating data regression models, using predictive data modelling, analyzing data ,fraud detection, time series analysis, survival analysis, clustered data analysis and recommendation systems. Involved in building large scale data pipeline using the ETL tools. Hands on experience on major components in Hadoop Ecosystem Hadoop, HDFS, HIVE.

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