Data Analytics with R
Data Analytics, in today’s world, has opened up a plethora of opportunities for industries and businesses across the world. It has made it possible to find answers to solutions, uncover trends and patterns that were impossible to have been noticed otherwise.
Travel and hospitality, Retail, Healthcare, Government, Banking and Financial sectors are some of the industries that find wide use of Data Analytics. R is a great open source tool that offers the most comprehensive statistical analysis packages. This course will teach you to use R for Big data analytics while exploring and exploiting the features that this tool has to offer!
Why R for Data Analytics?
- Like we said, the wide statistical analysis package collection including standard statistical tests, models and analysis make it a great choice with Data Analysts.
- Flexibility in managing and manipulating data.
- R offers excellent graphical capability.
- Integration with different programming languages like Java, Ruby, C++, Python.
Why should you learn R?
- R runs on all platforms – Windows, PC, Mac, Linux and more.
- R makes statistics easy to learn and fun.
- Learn R and become the in-demand Data Scientist, now!
- Engineering and MCA Graduates who are working as IT professionals and are looking to venture into Data Analytics.
- Some knowledge of Python and Hadoop will be great!
- Candidates should have minimum 60% marks throughout their academic session (10th/12th/Graduation).
SAS® Base syllabus
Data analytics with R syllabus1. Introduction to the R language:
- • SAS versus R
- • R, S, and S-plus
- • Obtaining and managing R
- • Objects - types of objects, classes, creating and accessing objects
- • Arithmetic and matrix operations
- • Introduction to functions
- • Reading and writing data
- • R libraries
- • Functions and R programming
- – the if statement
- – looping: for, repeat, while
- – writing functions
- – function arguments and options
- • Basic plotting
- • Manipulating the plotting window
- • Advanced plotting using lattice library
- • Saving plots
- • Model formulae and model options
- • Output and extraction from fitted models
- • Models considered:
- – Linear regression: lm()
- – Logistic regression: glm()
- – Poisson regression: glm()
- – Survival analysis: Surv(), coxph()
- – Linear mixed models: lme()
- • Extensions of topics discussed in lectures 1, 2 and 3 based on a course survey
- – Data management (importing, subsetting, merging, new variables, missing data
- – Plotting
- – Loops and functions
- • Further topics to be determined by student interest/requirements but may include
- – Migration SAS to R
- – Plotting and Graphics in R
- – Writing R functions, optimizing R code
- – Bioconductor, analysis of gene expression and genomics data.
- – More on linear models
- – Multivariate analysis, Cluster analysis, dimension reduction methods (PCA).