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Data Scienec with R programming - Syllabus

Machine Learning with R programe training in Pallikaranai, Chennai

Machine Learning with R course dives into the basics of R programme. Once familiar with R basics, then drive into Statistical inference. Next one will learn about Supervised vs Unsupervised Learning, and do a comparison of each with metrics and Evaluation model.


Data Science with R Syllabus

 

Module 1: Introduction to Data Science

  • What is Data Science?
  • What is Machine Learning?
  • What is Deep Learning?
  • What is AI?
  • Data Analytics & it’s types

Module 2: Introduction to R 

  • What is R?
  • Why R?
  • Installing R
  • R environment
  • How to get help in R
  • R Studio Overview

Module 3: R Basics 

  •  Environment setup
  • Data Types
  • Variables Vectors
  • Lists
  • Matrix
  • Array
  • Factors
  • Data Frames
  • Loops
  • Packages
  • Functions
  • In-Built Data sets

Module 4: R Packages  

  • Data Visualization
    • DataExplorer
    • esquisse
  • Machine Learning
    • MLR
    • parsnip
    • Ranger
    • purrr
  • Other Miscellaneous R Packages
    • rtweet
    • Reticulate
  • More R Packages!
    • InstallR
    • GitHubInstall

Module 5: Importing Data 

  • Reading CSV files
  • Writing data to csv file
  • Reading data from RDBMS 
  • Writing data into RDBMS

Module 6: Manipulating Data

  • Selecting rows/observations
  • Rounding Number
  • Selecting columns/fields
  • Merging data
  • Data aggregation
  • Data munging techniques

Module 7: Statistics Basics 

  • Central Tendency
    • Mean
    • Median
    • Mode
    • Skewness
    • Normal Distribution
  •  Probability Basics
    • What does mean by probability?
    • Types of Probability
    • ODDS Ratio?
  • Standard Deviation
    • Data deviation & distribution
    • Variance
  • Bias variance Trade off
    • Underfitting
    • Overfitting
  • Distance metrics
    • Euclidean Distance
    • Manhattan Distance
  • Outlier analysis
    • What is an Outlier?
    • Inter Quartile Range
    • Box & whisker plot
    • Upper Whisker
    • Lower Whisker
    • Scatter plot
    • Cook’s Distance
  • Missing Value treatments
    • What is a NA?
    • Central Imputation
    • KNN imputation
    • Dummification
  • Correlation
    • Pearson correlation
    • Positive & Negative correlation

Module 8: Error Metrics 

  • Classification
    • Confusion Matrix
    • Precision
    • Recall
    • Specificity
    • F1 Score
  • Regression
    • MSE
    • RMSE
    • MAPE

Module 9: Machine Learning - Supervised Learning

  • Linear Regression
    • Linear Equation
    • Slope
    • Intercept
    • R square value
  •  Logistic regression
    • ODDS ratio
    • Probability of success
    • Probability of failure
    • ROC curve
    • Bias Variance Tradeoff

Module 10 Machine Learning - Unsupervised Learning 

  • K-Means
  • Hierarchical Clustering

Module 11: Machine Learning using R 

  • Random forest
  • Naïve Bayes


R is a prominent in data analytics and data science for statistical computing even today. due to the advancement of Machine Learning, most of Deep Learning framework easily integrated with Python. In recent years Python is become the most populr choice in Data Science and Data Analytics. Interested in learning Python?

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