Data Science with Statistical Software R

Introduction and Basic Statistics with R


Date: Module wise

Venue: Godrej Hall, KAR Campus, Hyderabad + Video Conference.



Target audience:

Basic science researcher (PhD Students), all optometry faculty, Ophthalmology fellows/residence/DNB, BLSO student and anyone interested in Bio-Statistics.


Aims and Scope:

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of “Data Science with R” is to help you learn the most important tools in R that will allow you to do data analysis. With the knowledge gained in this work-shop, you will be ready to undertake your first very own data analysis.

· Introduction to statistical software R and basic data management with R.
· Applications of R to specific disciplines such as biology, epidemiology, genetics, and the ophthalmology.
· Combine statistical and machine learning techniques with R programming to analyze and interpret complex data.

· Graphical data analysis with R (ggplot2).
· Descriptive statistics, One- and two-sample tests, Regression and correlation Univariate and Multivariate data analysis with R

Course outline


Section Name

Topic Name


Induction date


Introduction to R and R Environment

About R



9AM To 12 Noon

Installation of R

Session management

About working directory

The graphics subsystem

R programming

Data Entry

R Data Types

Arithmetic in R

Using R as a calculator (+, -, *, / …etc)

Variable assignment

Basic Datatypes in R(Numeric, integer, character, logical and complex)

Data type coercion

Basic Functions (class, dim, str, summary, ls, remove, as.*, is.* …etc)


Creating vector

Naming Vector

Vector selections

Adding elements to vector

Update elements of vector

Delete elements of vector

Functions (c(), names() …etc)



Section Name

Topic Name


Induction date


Matrices and arrays

Categorical variables



9AM TO 12 Noon

Create matrix

Naming a matrix

Arithmetic with matrix

Adding row

Adding column

Selection of matrix elements

Insert /delete/update matrix elements

Transpose matrix

Combine rows of matrix

Combine columns of matrix


Categorical variables

Continuous variables

What is factor

Factor Levels in customized format

Nominal factors

Ordinal factors

Data Frame

What is data frame

Creating Data frame

Add /delete/update data frame elements

Summary function understanding (Mean, median, min, max, 1st QU, 3rd Qu …etc)

Functions (head, tail, sort, order, nrow, ncol … etc)


Conditional selection

Grouped data and data frames

Graphics systems in R

Base graphics

Grid graphics

Lattice graphics

ggplot2 graphics


Section Name

Topic Name


Induction date


Descriptive statistics and graphics

Summary statistics for a single group



9AM TO 12 Noon

Summary statistics by groups


Graphical display of distributions

Base graphics

Grid graphics

Lattice graphics

ggplot2 graphics (with Mr. Ganesh Jonnadula)

One- and two-sample tests

One-sample t test

Wilcoxon signed-rank test

Two-sample t test

Comparison of variances

Two-sample Wilcoxon test

The paired t test

The matched-pairs Wilcoxon test

Regression and correlation

Simple linear regression


Prediction and confidence bands



Multiple regression

Plotting multivariate data


Prediction and confidence bands




Course Convener


Mr. Md Hasnat Ali




Thank you for considering Data science with statistical software R


Registrations for the workshop is now closed