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Fundamental Methods for Data Science in R

Target Audience
Expected Duration
Lesson Objectives
Course Number

R is a free software environment for statistical computing and graphics and has become an important tool in modern data science. In this course, you will learn the fundamental R methods that data scientists use in their everyday work.

Target Audience
Individuals with statistics and programming experience who wish to learn the methods of data science in R.


Expected Duration (hours)

Lesson Objectives

Fundamental Methods for Data Science in R

  • start the course
  • distinguish data science from statistics and computer science
  • identify some of the problems data scientists solve
  • use various sources of data to learn data science
  • use data frames to store data in tables in R
  • use the R str function to display the internal structure of data
  • use summary statistics to catch problems before data analysis in R
  • use the rjson R package to import json formatted files
  • use the foreach loop in R
  • reshape values in your data in R
  • join data frames using the merge function in R
  • use the transpose function "t" in R
  • aggregate data frames in R
  • perform a fixed value imputation and perform a list wise deletion imputation in R
  • perform an imputation using the impute functions from the Hmisc package in R
  • use the R cut function to turn continuous data into discrete categories
  • identify the most frequently used functions for data analysis in R
  • fit a linear model using lm function in R
  • computing ANOVA using the aov function in R
  • extract coefficients from a modeling function in R
  • extract the fitted values from a modeling function in R
  • extract the residuals from a modeling function in R
  • calculate the variance-covariance matrix in R
  • calculate a confidence interval in R using confint
  • fit a generalized linear model using the glm function in R
  • use the ggplot2 library to plot models in R
  • compute the t-test in R
  • perform a TukeyHSD test in R
  • use the predict function in R
  • create a time series in R
  • use the forecast package in R
  • use common statistical methods for data analysis in R
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