R Programming (Beginner)

Course Duration : 50 Hrs + Case Study
15000

About R Programming

R is the most powerful Statistical Analysis Software according to researchers ,data scientists and analytics Professionals.
R is an Open Source Software available under GNU Project. R works on various operating system (Cross-Platform).
R has the most superior graphical capabilities for Data Visualization and Reporting.
R has the Compatibility and Readability for all file types.
R has most active User Community.


Course Overview

This foundation course focuses on the following key areas:

  • Creating datasets and Reading Datasets from other sources (TXT,EXCEL,CSV,SPSS,SAS)
  • Data Manipulation and Management
  • Basic Statistics in R [Measures of Central Tendency, Dispersion, Correlation, Regression]
  • Graphics in R [Base and Advanced Graphics]
  • Advances Statistics in R [Building Models in R-Linear Regression, GLM Regression, Logistic Regression]

What We Offer

Training under the guidance of 20+ years experienced Data Scientist with post graduation from IIT, PhD from Boston University, and 40+ research papers on Data Science.
After training, Internship at our Development Partner’s house (Ideal Analytics/ ArcVision) in real-time/live project work.
Case studies on real industry data
Classroom training with flexible timing
Customized/On-demand training
Unlimited access to exclusive Study Materials on Cloud

Chapter-1: Getting Started

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

 

Chapter-2: Basic Building Blocks in R

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

 

Chapter-3: Advanced Data Structures in R

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

 

Chapter-4: Reading Data into R

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

 

Chapter-5: Making Statistical Graphs

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 Creating Line plots
5.5 Control colour and shapes
5.6 Add themes to graphs

 

Chapter-6: Basics of Programming

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

 

Chapter-7: Data Munging

7.1 Learning Objectives
7.2 Repeating Matrix Operations – the apply function
7.3 Repeating List Operations
7.4 The mapply function
7.5 The aggregate function
7.6 The plyr package
7.7 Combining Datasets
7.8 Joining Datasets
7.9 Switch storage paradigms

 

Chapter-8: Manipulating Strings

8.1 Learning Objectives
8.2 Combine String together
8.3 Extract Text

 

CHAPTER-9: Basic Statistics

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

 

CHAPTER-10: Linear Models

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

 

CHAPTER-11: SQL & R Integration

11.1 Concept of SQL
11.2 SQL Operation
11.3 SQL Sever and R Studio Integration
11.4 Producing statistical graphs charts by importing data from Sql Server

Criminal Data Analysis

In the last few years most of the American cities have recorded a low crime rate. However, San Francisco Bay area saw an up surge in crimes reported in these years. The need for anticipating and take preventive measures have become the utmost priority for the local government authorities. The crime records of the area for the last few years need to be mined , analyzed and used to forecast the crime that can be committed in future. Our Objective is to find out

1.How many crimes have been reported in each category of crime?
2.Which day of the week sees the highest number of crimes recorded ?
3.Frequency of crimes resolved to that of pending.
4.How many crimes have been reported by each PdDistrict ?
5.Predict the Crime Category

Tania Chakraborty

In-house Faculty (R & SAS)

Tania, with a background in engineering, have 3+ years of hands on working experience on various Analytics tools, mainly SAS & R. She played a major role in the student data analysis of two entire countries, Dominica & St. Kitts, on a popular student management software “openSIS”.
Other than that she has worked on various other data analysis projects like, Data Analysis on US Economic Indices, Twitter Sentimental Analysis, GDP rates etc.
Simultaneously with project work, she provides training on Big Data analytics using Hadoop and R, Base SAS & Advanced SAS. She has already educated over hundred high profile MNC professionals on Data Analytics. She is the most junior but most appreciated faculty of our team.

Debajyoti Chakraborty

In-house Faculty (R & SAS)

Debajyoti, a Statistical Analyst, Member of Actuarial Society of India, Analytics Trainer on Statistical Softwares - SAS,R,Ms Excel with basic query language knowledge on SQL. Graduate in Statistics with Maths and Computer Science as other subjects. Having over 3yrs of work experience as Data Analyst. In Statistical Analyst role he has worked on multiple industry projects including dashboarding and analytics implementation for Retail and Healthcare projects. Also, as an Actuarial Analyst he assisted in Claim Analytics. As an Analytics Trainer, he is providing Analytics training to Industry Professionals and Academic Students on Statistical Software packages - SAS, R, MS Excel (Beginner to Advance) and SPSS, and overseeing Data Analysis projects undertaken by students and knowledge sharing for successful completion of projects on time.