Data Analytics with R

Data Analytics with R
Duration 30 Hours
Objective The Data Analytics with R program has been designed to give you in-depth knowledge of data analytics using R programming.
Who Should Attend?
  1. Any professionals considering data analytics as their career
  2. Professionals working as business analytics using extensive data
  3. Graduates looking to establish a career in analytics and data science
  4. Anyone with a genuine interest in the data analytics field
  5. Anyone who wants to effectively use data in decision making
Essentials Graduates / post graduates from any discipline & studied mathematics or statistics during their graduation Laptop with minimum of 8 GB RAM
Course Program
  • Getting a subset of a data structure
  • Making a vector filled with values
  • Information about variables
  • Working with NULL, NA, and NaN
  • Generating random numbers
  • Generating repeatable sequences of random numbers
  • Saving the state of the random number
  • Rounding numbers
  • Comparing floating point numbers
  • Loading data from a file
  • Loading and storing data with the keyboard
  • Running a script
  • Writing data to a file
  • Writing text and output from analyses to a file
  • Sorting
  • Randomizing order
  • Converting between vector types
  • Finding and removing duplicate records
  • Comparing vectors or factors with NA
  • Recoding data
  • Mapping vector values
  • Renaming levels of a factor
  • Re-computing the levels of factor
  • Changing the order of levels of a factor
  • Renaming columns in a data frame
  • Adding and removing columns from a data
  • Reordering the columns in a data frame
  • Merging data frames
  • Comparing data frames
  • Re-computing the levels of all factor
  • Converting data between wide and long
  • Summarizing data
  • Converting between data frames
  • Calculating a moving average
  • Averaging a sequence in blocks
  • Finding sequences of identical values
  • Filling in NAs with last non-NA values
  • Summarizing data
  • Descriptive statistics
  • Frequencies
  • T-test
  • Frequency tests
  • Logistic regression
  • Survival analysis
  • Robust and time series models
  • Regression and correlation
  • Multiple regression
  • Homogeneity of variance
  • Inter-rater reliability
  • Power Analysis
  • Nonparametric Statistics
  • Pie Charts
  • Line Charts
  • Bar graph
  • Boxplots
  • Scatterplots
  • Density Plots
  • Dot Plots
  • ggplot2 full package
Trainers Profile An expert with 5+ years of experience in R programming
Course Fee Includes
  • Three days of instructor led class room training
  • Course Material
  • Case study with examples
  • Refreshment at the venue