Data Science with R Training

Module 1: Introduction to Data Science (Duration-1hr)

  • 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 (Duration-1hr)

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

Module 3: R Basics (Duration-5hrs)

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

Module 4: R Packages (Duration-2hrs)

  • DMwR
  • Dplyr/plyr
  • Caret
  • Lubridate
  • E1071
  • Cluster/fpc
  • Data.table
  • Stats/utils
  • Ggplot/ggplot2
  • Glmnet

Module 5: Importing Data (Duration-1hr)

  • Reading CSV files
  • Saving in Python data
  • Loading Python data objects
  • Writing data to csv file

Module 6: Manipulating Data (Duration-1hr)

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

Module 7: Statistics Basics (Duration-11hrs)

  • 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 (Duration-3hrs)

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

Module 9: Machine Learning

Module 10: Supervised Learning (Duration-6hrs)

  • 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 11: Unsupervised Learning (Duration-4hrs)

  • K-Means
  • K-Means ++
  • Hierarchical Clustering

Module 12: Machine Learning using R (Duration-10hrs)

  • Linear Regression
  • Logistic Regression
  • K-Means
  • K-Means++
  • Hierarchical Clustering – Agglomerative
  • CART
  • 5.0
  • Random forest
  • Naïve Bayes
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