Course includes Live scenarios, Case Studies, Project, Assessments, Mock Interview.
Study Material PPTs, Doc, Data, PDFs etc.
TARGET AUDIENCE:
Any graduate - No prior knowledge of Data Science / Analytics is required.
WHAT IS R?
R is open source data analysis software: and widely uses by Data scientists, statisticians, Researchers and Data analysts—anyone who needs to make sense/insight of data can use R for Statistical Analysis, Data visualization, and Predictive Modeling. R is created by Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand in the 1990s as a statistical platform for their students, and thus it has been extended over the decades by thousands of user-created libraries/packages. R is a programming language: An object-oriented language created by statisticians, R provides objects, operators, and functions that allow users to explore, model, and visualize data. R is a vector language, so anyone can add functions to a single Vector without putting in a loop. And at the same time R is powerful and faster than other languages, we can easily implement Machine Learning algorithms in a fast and simple way
JOB PROFILES IN R:
R Programmer
Data Analyst/Miner
Data Modeler
Data Scientist
ML specialist
NLP specialist and many more.
*****COURSE CONTENT*****
FUNDAMENTAL OF STATISTICS:
Population and sample
Descriptive and Inferential Statistics
Statistical data analysis
Variables
Central Tendency, Sample and Population Distributions
Central Limit Theorem (CLT)
Estimation & Confidence interval
Normal Distribution
Skewness.
Boxplot
Standard deviation
Standard Error
Hypothesis testing
P-value
Scatter plot and correlation coefficient
Scales of Measurements and Data Types
Numerical Summarization
Outliers & Summary
Data Summarization
Visual Summarization
MODULE 1- INTRODUCTION TO R PROGRAMMING
Installing & starting with R
Basic and environmental features of R.
Calculations with R
Functions
Understanding R language and programming guidelines
Listing the objects in the workspace
Vectors
Extracting elements from vectors
Vector arithmetic
Simple patterned vectors
Missing values and other special values
Character vectors Factors
More on extracting elements from vectors
Matrices and arrays
Data frames
Dates and times
Assignments with Datasets
MODULE 2- INTRODUCTION TO DATA ANALYTICS:
This module introduces you to some of the important keywords in R like Business Intelligence, Business
Analytics, Data and Information. You can also learn how R can play an important role in solving complex analytical problems
This module tells you what is R and how it is used by the giants like Google, Facebook, etc. Also, you will learn use of 'R' in the industry, this module also helps you compare R with other software in analytics, install R and its packages.
Business Analytics, Data, Information Understanding Business Analytics and R Compare R with other software in analytics Install R Perform basic operations in R using command line
MODULE 3- IMPORT AND EXPORT DATA IN R
Importing data into R
CSV File
Excel File
Import data from text table
DATA SCIENCE USING R-PROGRAMMING
Topics
Variables in R
Scalars
Vectors
R Matrices
List
R – Data Frames
Using c, Cbind, Rbind, attach and detach etc. functions in R
Objects- Explicit Coercion- Summary- Names- Data Frames
MODULE 4- MANAGING DATA FRAMES WITH THE DPLYR PACKAGE:
The dplyr Package
Installing the dplyr package
select()
filter()
arrange()
rename()
mutate()
group_by()
%>%
Assignments
Business Scenario/Group Discussion
MODULE 5- LOOPS FUNCTIONS:
Looping on the Command Line
lapply()
sapply()
tapply()
apply()
Assignments
Business Scenerio/Group Discussion
MODULE 6- DATA MANIPULATION IN R OBJECTIVES:
In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting
in a data set, which is ready for any analysis
Thus using and exploring the popular functions required to clean data in R.
Topics
Data sorting
Find and remove duplicates record
Cleaning data
Merging data
Statistical Plotting
Bar charts and dot charts
Pie charts
Histograms
Box plots
Scatter plots
QQ plots
Assignments with Datasets
OBJECTIVES
Control Structure Programming with R
The for() loop
The if() statement
The while() loop
The repeat loop, and the break and next statements
Apply, Sapply, Lapply
Assignments with Datasets
FACTORS:
Using Factors
Manipulating Factors
Numeric Factors
Creating Factors from Continuous Variables
Convert the variables in factors or in others
RESHAPING:
Data Modifying
Data Frame Variables
Recoding Variables
The recode Function
Reshaping Data Frames
The reshape Package
Assignments with Datasets
MODULE 7- BASICS OF STATISTICS & LINEAR & MULTIPLE REGRESSION:
This module touches the base of Descriptive and Inferential Statistics and Probabilities & 'Regression Techniques'.
Linear and logistic regression is explained from the basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed.
Assessing the Accuracy of the Coefficient Estimates
