R Programming

R Programming

Courses Info

R Analytics

Course Highlights :-

 

  • All the training would be provided by Industry Experts who already works on R Analytics
  • Backup Class in case you miss any session.
  • Theory + Practical Training along with case studies in order to get better understanding of concepts.
  • Complete course material with no extra cost.
  • Free doubt clearing session after completion of the training.
  • Resume building by experts.
  • Feedback form filled by candidates after every class in order to maintain highest level of quality standards.

 

  • Overview of the R language
  1. Defining the R project
  2. Obtaining R
  3. Using the R console
  4. A sample R session

 

  • Generating R code
  1. Basic programming concepts
  2. Scripts
  3. Text editors for R
  4. Graphical User Interfaces (GUIs) for R
  5. Packages

 

  • Objects and data structures
  1. Variable classes (factor, numeric, logical, complex, missing)
  2. Vectors and matrices
  3. Data frames and lists
  4. Data sets included in R packages
  5. Summarizing and exploring data

 

  • Dealing with data
  1. Reading data from external files
  2. Storing data to external files
  3. Creating and storing R workspaces
  4. Basic exploratory graphics

 

  • Manipulating objects
  1. Mathematical operations
  2. Basic matrix computation
  3. Textual operations
  4. Searches, strings, and pattern matching
  5. Regular sequences
  6. Random sequences
  7. Sampling from distributions

 

  • Graphics
  1. More slicing and extracting data
  2. Basic plots
  3. Adding overlaid lines, text, etc.
  4. Graphical parameters
  • Data exploration

Summary graphics

 

  • Graphics, (advance)

Basic graphical troubleshooting

– Brief introduction to regression graphics

– Generating data

  • Programming (basic)

– Functions

– Control structures

– Debugging

 

  • Hypothesis testing and data handling

– t-tests

– ANOVA

– Sorting/rearranging data structures

 

  • Linear & logistic regression

– General modeling syntax

– Extracting model results

– Confidence intervals

– Graphics for regression

– Tabular displays

– Extracting model results

– Confidence intervals

– Regression diagnostics

 

  • Graphics (intermediate)

– 3D graphics

– Graphics presentation

– Interactive graphics

 

  • Graphics (advanced)

– Animations

– High-density data displays

– Heatmaps

– Partitioning graphics

 

  • Functions & resampling methods for model validation

– Applying functions

– Writing your own functions

– Modifying existing functions

– Permutation testing

– Bootstrapping

– Cross-validation methods