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Statistical Predictive Modelling and Applications

  • Free

  • online
  • Online |
  • Anytime
  • Certificate

In this course, you will learn three predictive modelling techniques - linear and logistic regression, and naive Bayes - and their applications in real-world scenarios.

The first half of the course focuses on linear regression. This technique allows you to model a continuous outcome variable using both continuous and categorical predictors.

This technique enables you to predict product sales based on several customer variables.

In the second half of the course, you will learn about logistic regression, which is the counterpart of linear regression, when the response variable is categorical.

You will also be introduced to naive Bayes; a very intuitive, probabilistic modeling technique.

Program Benefits

In this course, you will:

  • Discover how predictive models influence real-world business scenarios
  • Translate business challenges into predictive modeling solutions
  • Develop experience with implementing theoretic models in Python

Faculty

Dr Xuefei Lu
Lecturer in Predictive Analytics
Sofia Varypati
Course Tutor
Obinna Unigwe
Course Tutor
Dr Galina Andreeva
Senior Lecturer in Management Science

*Program faculty is subject to change

Curriculum

Week 1: Simple Linear Regression
Week 2: Multiple Linear Regression
Week 3: Extensions and Applications
Week 4: Introduction to Naive Bayes
Week 5: Logistic Regression
Week 6: Estimation and Comparison

Upcoming

This course will give you an overview of machine learning-based approaches for predictive modelling, including tree-based techniques, support vector machines, and neural networks using Python. These models ...
  • Free
  • Online
  • Certificate
This course will give you an overview of machine learning-based approaches for predictive modelling, including tree-based techniques, support vector machines, and neural networks using Python. These models form the ...
  • Free
  • Online
  • Certificate