Introduction to R for Data Science

April 30, 2016
By

(This article was first published on The Exactness of Mind, and kindly contributed to R-bloggers)

Branko Kovač Data Analyst at CUBE, Data Science Mentor at Springboard, Institut savremenih nauka, Data Science Serbia, and Goran S. Milovanović, [email protected], Data Science Serbia, are giving a free introductory course on R for Data Science in Belgrade, Serbia. All course materials – slides, R scripts, data sets, summaries and recommended readings – can be found on this page. 

The course is organized by Data Science Serbia in cooperation with Startit, Belgrade. Fifteen participants are working with us in Startit, Belgrade, Savska 5, each Thursday beginning 28. April 2016 19h CET in situ.

The course will be carried out through ten sessions (reproducible R code can be found at the following pages):

  • Session 1: Introduction to R

    Elementary data structures, data.frames + an illustrative example of a simple linear regression model. An introduction to basic R data types and objects (vectors, lists, data.frame objects). Examples: subsetting and coercion. Getting to know RStudio. What can R do and how to make it perform the most elementary tricks needed in Data Science? What is CRAN and how to install R packages? R graphics: simple linear regression with plot(), abline(), and fancy with ggplot2().

  • Session 2: Vectors, Matrices, Data Frames

    Introduction to vectors, matrices, and data frames in R. R is a vector programming language, which means you will be using vectors, matrices, and n-dimensional arrays a lot. Vectorizing your code means enhanced performance in terms of speed. Data frame objects in R are elementary carriers of most of your data in R; unlike vectors and matrices, data frames can encompass various data types.

  • Session 3: Data Frames, Factors, and Objects in R.
  • Session 4: Data Structures + Control Flow = Programs. Functions in R.
  • Session 5: Structuring Data: String manipulation in R.
  • Session 6: Introduction to GLM: Correlations and Linear Regression in R.
  • Session 7: Introduction to GLM: Multiple Regression in R.
  • Session 7: Extending the Scope of the GLM: Binomial Logistic Regression in R.
  • Session 8: Extending the Scope of the GLM: Multinomial Logistic Regression in R.
  • Session 9: Dimensionality Reduction: Multdimensional Scaling in R with Smacof.
  • Session 10: Non-parametric Methods in R.

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