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DBM4110: Data Analysis with R

(ANC-DBM4110.AJ1)
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Skills You’ll Get

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1

Getting Started with Regression and Simple Linear Regression

  • Going back to the origin of regression
  • Regression in the real world
  • Understanding regression concepts
  • Regression versus correlation
  • Discovering different types of regression
  • The R environment
  • Installing R
  • RStudio
  • R packages for regression
  • Association between variables – covariance and correlation
  • Searching linear relationships
  • Least squares regression
  • Creating a linear regression model
  • Modeling a perfect linear association
2

MLR and Logistic Regression

  • Multiple linear regression concepts
  • Building a multiple linear regression model
  • Multiple linear regression with categorical predictor
  • Gradient Descent and linear regression
  • Polynomial regression
  • Understanding logistic regression
  • Generalized Linear Model
  • Multiple logistic regression
  • Multinomial logistic regression
3

Data Preparation and Avoiding Overfitting Problems

  • Data wrangling
  • Finding outliers in data
  • Scale of features
  • Discretization in R
  • Dimensionality reduction
  • Understanding overfitting
  • Feature selection
  • Regularization
4

Regression Models and When Curving is Much Better

  • Robust linear regression
  • Bayesian linear regression
  • Count data model
  • Nonlinear least squares
  • Multivariate Adaptive Regression Splines
  • Generalized Additive Model
  • Regression trees
  • Support Vector Regression
5

Regression Analysis in Practice

  • Random forest regression with the Boston dataset
  • Classifying breast cancer using logistic regression
  • Regression with neural networks

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