Data Science with R

64 students enrolled

R is an open source interpreted programming language for statistical computing and graphical visualization. R is widely used for data mining, statistical software, and data analysis.

Once, students are conversant with R, a detailed study of data science which includes data mining & machine learning, starts using R. Machine Learning covers the linear & generalized linear models, KNN, Naïve Bayes, Tree based models, SVM, K-means, Association rule, performance measures, dimension reduction techniques, randomization, cross validation, bootstrapping, ROC & AUC, and confusion matrix.

This course prepares one on the practical applications of Machine Learning algorithms & techniques using R

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DESCRIPTION

R is an open source interpreted programming language for statistical computing and graphical visualization. R is widely used for data mining, statistical software, and data analysis.

This 100-hour classroom course introduces the students with fundamentals and advanced level of R programming, right from data types, vectors, matrices, controls, loops, functions, packages, importing data, visualization, to packages like - dplyr, caret, tidyr, stringr, ggplot2, shiny. Once, students are conversant with R, a detailed study of data science which includes data mining & machine learning, starts using R. Machine Learning covers the linear & generalized linear models, KNN, Naïve Bayes, Tree based models, SVM, K-means, Association rule, performance measures, dimension reduction techniques, randomization, cross validation, bootstrapping, ROC & AUC, and confusion matrix. 

This course prepares one on the practical applications of Machine Learning algorithms & techniques using R. Students can start solving the real-world problems.

  • Project Work
  • Installing R studio, programming basics, features, data types, vectors, matrices, controls, loops, and functions
  • Packages:

                + Importing data from excel, web & databases

                + Data pre-processing – Missingness, outliers, errors

                + Manipulating data - Imputing

                + Visualization & spatial packages

                + Modelling data using various data mining algorithms 

                + Report & result – R markdown, shiny

                + Timeseries & financial data – zoo, xts, quantmod

                +Project using R and Machine Learning concepts

  • Basic knowledge of Linear Algebra, Calculus, Statistics, and Probability.
  • Knowledge of basics of programming

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