R

Data Science with R Programming

Data Analytics with R makes you an expert in R programming, Exploratory Data Analysis, Data Manipulation, Data Mining, Machine Learning Algorithm (Regression, classification), Data Visualization, Sentiment Analysis, Text Mining, Image processing and web application using R Studio. R training lets you learn R programming language that is deployed for varied purposes like graphics representation, statistical analysis and reporting. With this online R Programming & data analysis training you will be able to get a clear understanding of the core concepts, import data in various formats for statistical computing, data manipulation, business analytics, machine learning algorithms and data visualization. You will learn about the various functions, data structures, variables and flow of control. Learn how to go about doing R integration with Hadoop through practical R exercises.

  • Mon – Fri ( 6 Weeks ) | 06.30 AM - 06.30 PM Time (IST) (any 2 hours)

  • Sat – Sun ( 8 Weeks ) | 07.30 AM - 7:30 PM Time (IST) (any 3 hours)

Why this course?

  • 80% of the organizations in India consider Data Analytics has the principal component which enhances the business performance.
  • The average salary for a Senior Data Scientist skilled in R is Rs.12,75,000 (Payscale salary data)
  • Data Analytics is one of the newest fields which is emerging in response to the exponential volume of data being captured and explored every day.
  • It is a free and open source programming language used to execute advanced data analysis tasks.


Objective of the course

By the end of this Data Analytics course, you will be able to

  • Learn Data Science concepts of R and functioning of R-Calculator
  • Understand various functions like Stack, Merge and Strsplit
  • Learn to create Pie charts, plots and vectors
  • Assign value to variables, generate repeat and factor levels
  • Performing sorting, analyze variance and the cluster
  • ODBC Tables reading, linear and logistic regression
  • Understand database connectivity
  • Deploy R programming for Hadoop applications
  • Master statistics and R programming concepts
  • Learn Data Manipulation and apply advanced visualization methods
  • Gain ability to handle various input files, perform suitable analytics
  • Able to perform sentiment analysis and process images using R
  • Applying suitable Machine Learning Algorithm to predict output

Who should take Data Analytics?

The Data Analytics with R, is designed for everyone who wants to enhance their technical skills in data-wrangling/manipulation, Machine learning and data visualization.

The following professionals can take this course

  • Software engineers and data analysts
  • Business intelligence professionals
  • SAS developers wanting to learn open source technology
  • Those aspiring for a career in data science
  • Finance or Banking professionals
  • IT and Engineering professionals
  • Business Intelligence/Development/Analysts
  • Marketing and Operations professional
  • Supply Chain & Procurement

Requirements

No prior programming skills or statistical knowledge or experience required. Only a strong desire towards your goal!

R-Programming Course Syllabus

  • 1.1 Learning objectives
  • 1.2 Download and Install R and R Studio
  • 1.3 Working in the R Windowing Environment
  • 1.4 Install and Load Packages
  • 2.1 Learning Objectives
  • 2.2 R as a Calculator
  • 2.3 Work with variables
  • 2.4 Understand Data Types
  • 2.5 Store Data in Vectors
  • 2.6 Call Functions
  • 3.1 Learning Objectives
  • 3.2 Create and Access Information in Data Frames
  • 3.3 Create and Access Information in Lists
  • 3.4 Create and Access Information in Matrices
  • 3.5 Create and Access Information in Arrays
  • 4.1 Learning Objectives
  • 4.2 Reading CSV Files
  • 4.3 Understanding Excel is not easily accessible in R
  • 4.4 Read from Databases
  • 4.5 Read Data files from other Statistical Tools
  • 4.6 Load binary R files
  • 4.7 Load Data included with R
  • 4.8 Scrape Data from the web
  • 5.1 Learning Objectives
  • 5.2 Using Datasets for creating Graphs.
  • 5.3 Making Histograms , Bar graphs , Line graphs,Scatterplots,Boxplots etc with Base Graphics
  • 5.4 Introduction to ggplot2
  • 5.5 Histograms and density plots with ggplot2
  • 5.6 Scatterplots with ggplot2
  • 5.7 Box and violin plots with ggplot2
  • 5.8 Creating Line plots
  • 5.9 Control colour and shapes
  • 5.10 Add themes to graphs
  • 6.1 Learning Objectives
  • 6.2 The Classic “Hello World” Example
  • 6.3 Basics of Function Arguments
  • 6.4 Return a Value from a Function
  • 6.5 Flexibility with the do call
  • 6.6 If Statements for controlling Program Flow
  • 6.7 If-Else Statements
  • 6.8 Multiple checks using Switch
  • 6.9 Checks on entire Vectors
  • 6.10 Check Compound Statements
  • 6.11 Iteration- for and while loop
  • 6.12 Control loops with Break and Next
  • 6.1 Learning Objectives
  • 6.2 The Classic “Hello World” Example
  • 6.3 Basics of Function Arguments
  • 6.4 Return a Value from a Function
  • 6.5 Flexibility with the do call
  • 6.6 If Statements for controlling Program Flow
  • 6.7 If-Else Statements
  • 6.8 Multiple checks using Switch
  • 6.9 Checks on entire Vectors
  • 6.10 Check Compound Statements
  • 6.11 Iteration- for and while loop
  • 6.12 Control loops with Break and Next
  • 7.1 Learning Objectives
  • 7.2 Repeating Matrix Operations – the apply function
  • 7.3 Repeating List Operations
  • 7.4 The mapply, sapply, lapply, tapply functions. where & how to use
  • 7.5 The aggregate function
  • 7.6 The plyr package
  • 7.7 Combining Datasets
  • 7.8 Joining Datasets
  • 7.9 Switch storage paradigms
  • 8.1 Learning Objectives
  • 8.2 Combine String together
  • 8.3 Extract Text
  • 8.4 Regular Expressions
  • 8.5 Grep, Grepl, gsub
  • 8.6 Identify special or specific characters
  • 9.1 Learning Objectives
  • 9.2 Drawing numbers from Probability Distributions
  • 9.3 Summary Statistics-Mean, Variance,SD,Correlation
  • 9.4 Compare samples with t-tests and Analysis of Variance
  • 10.1 Learning Objectives
  • 10.2 Fit simple Linear models
  • 10.3 Exploring the Data
  • 10.4 Fit multiple Regression Models
  • 10.5 Fit Generalised Linear Models(GLM)
  • 10.6 Fit Logistic Regression
  • 10.7 Fit Poisson Regression
  • 10.8 Analyze Survival Data
  • 10.9 Asses Model Quality and Residuals
  • 10.10 Compare Models
  • 11.1 k means - Unsupervised Algorithm
  • 11.2 Different Statistical methods to identify Significant & Insignificant Variables
  • 11.3 Optimization Algorithms
  • 11.4 Fit Non-Linear Least Squares
  • 11.5 Decision Tree
  • 11.6 Naive Bayes
  • 11.7 Random Forest
  • 12.1 Learning Objectives
  • 12.2 Understanding ACFs and PACFs
  • 12.3 Fit and Assess ARIMA Models
  • 13.1 Learning Objectives
  • 13.2 Text Extraction & manipulation
  • 13.3 Sentiment Analysis
  • 13.4 Social Media Analytics- Case Studies
  • 14.1 Concept of SQL
  • 14.2 SQL Operation
  • 14.3 SQL Sever and R Studio Integration
  • 14.4 Producing statistical graphs charts by importing data from Sql Server