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Professional R Programmer Course

Course Overview

1,Why Professional R Programmer Course ?

R is growing into one of the most popular and in-demand analytics tool given its open-source & free nature, advanced analysis and visualization abilities. This R tutorial has been designed to make you proficient in implementing analytics studies in R. This is a 15 hours video based, completely online R tutorial where you can view the videos at your own pace. These videos will take you through the fundamentals of R programming for analytics in a structured manner. A problem solving based approach has been used through the R tutorial video content to provide holistic, practical and hands-on learning.

2, What are the requirements??

This is a self-learning course through recorded videos where you can learn at your own time and pace. Get faculty support through forums, email or calls for your doubts..

3, What am I going to get from this course???

  • Become proficient in manipulating, analyzing and visualizing data through the powerful R language.
  • Experienced analysts can keep themselves up to date with changing trends in the industry and add R programming skills to their arsenal.
  • Analytics novices can gain a starting advantage by learning R programming skills which are in high demand but short supply.
  • Relevant examples and cases make the learning more effective and easier Gain hands-on knowledge through the problem solving based approach of the course along with working on assignments and solving quizzes.
  • You can learn at your own pace and with life-time access you can go through the modules as many times as you like.
  • The video based nature of the course ensures a more effective and engaging learning experience for you through demonstration and observation

Modules Overview

  • Downloading and Installing R
  • Getting Help on a function
  • Viewing Documentation
  • General issues in R
  • Packages Management
  • Data Types
  • Subsetting
  • Reading tabular data files
  • Reading from csv files
  • Initializing a data frame
  • Control structures
  • Selecting data frame cols by position and name
  • Changing directories
  • Re-directing R output
  • Creating a bar chart, dot plot
  • Creating a scatter plot, pie chart
  • Creating a histogram and box plot
  • Other plotting functions
  • Plotting with base graphics
  • Plotting with Lattice graphics
  • Plotting and coloring in R
  • Appending data to a vector
  • Combining multiple vectors
  • List management
  • Merging dataframes
  • Data transformation
  • Strings and dates
  • Outlier detection
  • Handling NAs and Missing Values
  • Matrices and Arrays
  • Logical operations
  • Relational operators
  • Accessing Variables
  • Matrix Multiplication and Inversion
  • Managing Subset of data
  • Character manipulation
  • Data aggregation
  • Subscripting
  • Flow Control: For loop
  • If condition
  • While conditions and repeat loop
  • Debugging tools
  • Concatenation of Data
  • Combining Vars, cbind, rbind
  • Sapply, apply, tapply functions
  • Basics of SQL
  • RODBC and DBI Package
  • Performing queries
  • Advanced Data Handling
  • Combining and restructuring data frames
  • Simple linear regression
  • Multiple Regression model
  • Logistic regression
  • Hierarchical Clustering
  • K-Means Clustering
  • PCA for Dimensionality Reduction