Data Science using R

Data Science using R

Course Description

1. Introduction to R

  • R Essentials – Vectors, Matrices, Factors, Dataframes, Lists, Functions, Apply Family
  • Visualization – ggplot2, Visualization using Graphics, Histograms, Line/Bar/Box Plots
  • Data Wrangling – Filter, Mutate and Arrange data using gapminder and dplyr packages
  • Grouping and Summarizing – summarize and group_by
  • Loops, Control Flow

2. Data Preparation in R

  • Importing Data in R – Flat Files, Excel, Databases, Web or statistical softwares (SAS)
  • Tidying, Combining and Cleaning Data for Analysis

3. Statistics essentials

  • Hypothesis Testing
  • Parametric Test
  • Non-Parametric Test
  • Data Sampling
  • Confidence Intervals and Significance Levels

4. R Environment Setup and Essentials

  • Installing Packages
  • Working with environments

5. Supervised Learning

  • Classification – KNN, Naïve Bayes, SVM
  • Regression – Linear, Logistic, GLMs, Multiple
  • Model Evaluation – RMSE, AUC, ROC, MSE, R-square, Adjusted R-square
  • Model Tuning
  • Data Preprocessing – Missing Data Handling, Data Imputation, Centering and Scaling

6. Unsupervised Learning

  • Clustering – K-means, Hierarchical Clustering and t-SNE
  • Dimension Reduction – Principal Component Analysis

7. Tree-Based Models in R

  • Classification and Regression Trees (CART)
  • Decision Trees
  • Random Forests
  • Boosting
  • Model Tuning
  • Bias-Variance Tradeoff – Understanding the concept of overfitting and under-fitting



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Office 1.05, 1st Floor, Building 2,Croxely Business Park, Watford, WD18 8YA


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