Data Science with R

Data Science with R course is offered by The XL Academy. The XL Academy is a capability-building and training solutions organization with deep industry experience and a flair for coaching. We are focused on developing skills in Basic & Advanced Analytics to emerge as “Industry Ready” professionals and enhance their career opportunities.

Course Content:

• 1. Overview of the R language
• Defining the R project
• Obtaining R
• Using the R console
• A sample R session
• 2. Generating R code
• Basic programming concepts
• Scripts
• Text editors for R
• Graphical User Interfaces (GUIs) for R
• Packages
• 3. 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
• 4. Dealing with data
• Reading data from external files
• Storing data to external files
• Creating and storing R workspaces
• Basic exploratory graphics
• 5. Manipulating objects
• Mathematical operations
• Basic matrix computation
• Textual operations
• Searches, strings, and pattern matching
• Regular sequences
• Random sequences
• Sampling from distributions
• 6. Graphics
• More slicing and extracting data
• Basic plots
• Adding overlaid lines, text, etc.
• Graphical parameters
• Data exploration
• Summary graphics
• 7. Graphics, (advance)
• Basic graphical troubleshooting
• Brief introduction to regression graphics
• Generating data
• 8. Programming (basic)
• Functions
• Control structures
• Debugging
• 9. Hypothesis testing and data handling
• T-tests
• ANOVA
• Sorting/rearranging data structures
• 10. Linear & logistic regression
• General modeling syntax
• Extracting model results
• Confidence intervals
• Graphics for regression
• Tabular displays
• Extracting model results
• Confidence intervals
• Regression diagnostics
• 11. Graphics (intermediate)
• 3D graphics
• Graphics presentation
• Interactive graphics
• 12. Graphics (advanced)
• Animations
• High-density data displays
• Heatmaps & Partitioning graphics
• 13. Functions & resampling methods for model validation
• Applying functions
• Writing your own functions
• Modifying existing functions
• Permutation testing
• Bootstrapping
• Cross-validation methods