Statistical Predictive Modelling and Applications (edX)

Statistical Predictive Modelling and Applications (edX)
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Statistical Predictive Modelling and Applications (edX)
Learn how to apply statistical modelling techniques to real-world business scenarios using Python. 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.

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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.

This course is part of the Predictive Analytics using Python MicroMasters.


What you'll learn

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


Syllabus


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


Prerequisites

You should be familiar with an undergraduate level, or have a background, in mathematics and statistics. Previous experience with a procedural programming language is beneficial (e.g. Python, C, Java, Visual Basic).

Learners pursuing the MicroMasters programme are strongly recommended to complete PA1.1x Introduction to Predictive Analytics using Python and PA1.2x Successfully Evaluating Predictive Modelling on the verified track prior to undertaking this course.



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