# Data Science with Statistical Software R

## 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 Module Induction date Time Introduction to R and R Environment About R I 20-Jan-19 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) Vector 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 Module Induction date Time Matrices and arrays Categorical variables II 27-Jan-19 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 Factor 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) Indexing Conditional selection Grouped data and data frames Graphics systems in R Base graphics Grid graphics Lattice graphics ggplot2 graphics
 Section Name Topic Name Module Induction date Time Descriptive statistics and graphics Summary statistics for a single group III 03-Feb-19 9AM TO 12 Noon Summary statistics by groups Table 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