Advanced Predictive Modelling in R Certification Training

This training is intended for a broad audience as both, an introduction to predictive models as well as a guide to applying them, covering topics such as Ordinary Least Square Regression, Advanced Regression, Imputation, Dimensionality Reduction etc.  

Advanced Predictive Modelling in R Certification Training
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This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. Predictive modelling is emerging as a competitive strategy across many business sectors and can set apart high performing companies. Models such as multiple linear regression, logistic regression, auto-regressive integrated moving average (ARIMA), decision trees, and neural networks are frequently used in solving predictive analytics problems. Regression models help us understand the relationships among these variables and how their relationships can be exploited to make decisions.

After the completion of this training, you will be able to:

  • Understand Basics of Statistics using R
  • Explain Regression
  • Understand Simple, Multiple, Advanced and Logistic Regression
  • Perform model fitting using Linear Regression
  • Explain What is Heteroscedasticity?
  • Understand Binary Response Variable and Linear Probability Model
  • Explain Imputation
  • Understand Forecasting
  • Learn Neural Networks
  • Explain Dimensionality Reduction
  • Understands the algorithms associated with Dimensionality Reduction
  • Understand Survival Analysis

This course will introduce you to some of the most widely used predictive modelling techniques and their core principles which is designed for anyone who is interested in using data to gain insights and make better business decisions. The techniques discussed in this course are applied throughout all functional areas within business organizations such as accounting, finance, human resource management, marketing, operations, strategic planning etc. 

Программа курса:

  1. Basic Statistics in R
  2. Ordinary Least Square Regression
  3. Logistic Regression
  4. Advanced Regression
  5. Imputation
  6. Forecasting 
  7. Dimensionality Reduction
  8. Survival Analysis