R Training in Delhi NCR

About R
R is a free software like S (GNU) environment for statistical computing and graphics. It compiles and runs on a wide variety of Linux platforms ,UNIX platforms, Microsft Windows (XP, vista, windows 7)and MacOS. It provides , generate code to manipulate data, a wide variety of statistical and graphical techniques (factor analysis, linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, …). After long use in academia, R only recently began to appear in the business for statistical computing and graphics.

Course description
This course is about the R language for statistical applications. Participant will learn a basic repertoire of R skills and develop knowledge on how to expand those skills to apply statistical application . Participant will generate code to manipulate data, perform a variety of analyses, statistical test models, and create graphical presentations of results. The main objective is to develop R skills that can help answer Participant’s own research problem.
There are no formal prerequisites for this course, although Participant are expected to have a basic knowledge of statistics .The training material is specific to the R language; therefore, while previous programming experience will be helpful, but it is not essential.

Contact information


I.Overview of the R language
– Defining the R project
– Obtaining R
– Using the R console
– A sample R session
II. Generating R code
– Basic programming concepts
– Scripts
– Text editors for R
– Graphical User Interfaces (GUIs) for R
– Packages
III. Objects and data structures
– Variable classes (factor, numeric, logical, complex, missing)
– Vectors and matrices
– Data frames and lists
– Data sets included in R packages
– Summarizing and exploring data
IV. Dealing with data
– Reading data from external files
– Storing data to external files
– Creating and storing R workspaces
– Basic exploratory graphics

V. Manipulating objects
Mathematical operations
Basic matrix computation
-Textual operations
– Searches, strings, and pattern matching
– Regular sequences
– Random sequences
– Sampling from distributions
VI. Graphics
– More slicing and extracting data
– Basic plots
– Adding overlaid lines, text, etc.
– Graphical parameters
– Data exploration
– Summary graphics

VII. Graphics, (advance)
– Basic graphical troubleshooting
– Brief introduction to regression graphics
– Generating data
VIII. Programming (basic)
– Functions
– Control structures
– Debugging

IX. Hypothesis testing and data handling
– t-tests
– Sorting/rearranging data structures
X. Linear & logistic regression
– General modeling syntax
– Extracting model results
– Confidence intervals
– Graphics for regression
– Tabular displays
– Extracting model results
– Confidence intervals
– Regression diagnostics
XI. Graphics (intermediate)
– 3D graphics
– Graphics presentation
– Interactive graphics
XII. Graphics (advanced)
– Animations
– High-density data displays
– Heatmaps
– Partitioning graphics
XIII. Functions & resampling methods for model validation
– Applying functions
– Writing your own functions
– Modifying existing functions
– Permutation testing
– Bootstrapping
– Cross-validation methods

log on to www.iisastr.com
contact Ph: 9312506496
email: info@iisastr.com

For Time series data analysis using R syllabus log on to http://blog.iisastr.com/?p=342

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