Data Science with R

by The XL Academy Claim Listing

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.

₹14500

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img Duration

2 Months

Course Details

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
  • Bangalore Branch

    GK Arcade, 125/1/18, Ashoka Pillar Rd, 1st Block, Bangalore
  • Pune Branch

    East Court Phoenix Market City, L-29, Clover Park, Pune
  • Mumbai Branch

    7th Floor, Chandak Chambers, Andheri - Kurla Rd, Mumbai
  • Thane Branch

    16th Floor, A Wing, Dev Corpora Cadbury Junction, Thane
  • Gurgaon Branch

    90, Mehrauli-Gurgaon Rd, Industrial Development Area, Sector 14, Gurgaon
  • Noida Branch

    D-9, Vyapar Marg, Block D, Sector 3, Noida

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