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Statistical Predictive Modelling and Applications

Learn how to apply statistical modelling techniques to real-world business scenarios using Python.

Statistical Predictive Modelling and Applications

There is one session available:

After a course session ends, it will be archived.
Starts Jan 18, 2022
Ends Mar 15, 2022
Estimated 6 weeks
8–10 hours per week
Instructor-paced
Instructor-led on a course schedule
Free
Optional upgrade available

About this course

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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.

At a glance

  • Institution: EdinburghX
  • Subject: Data Analysis & Statistics
  • Level: Advanced
  • 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.

What you'll learn

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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

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

About the instructors

Frequently Asked Questions

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What type of activities will I complete on the course?
This course foregrounds self-directed and active ways of learning: reading, coding in Python, knowledge check quizzes and peer discussion. In addition, the course features videos that demonstrate relevant predictive analysis techniques and concepts.

What software will I be required to use?
All coding activities on this course will be hosted on Vocareum. You will be able to access this free software directly within the edX platform. There is no requirement to purchase further software in order to complete this course.

What do I need to complete the course?
For successful completion of this course, you will need access to a computer or mobile device and a reliable internet connection.

What is the University of Edinburgh Accessibility Guidance?

The University of Edinburgh is committed to providing online information and services accessible to all. Edx provide an accessibility statement which is available via the footer of all edx.org pages and includes an 'Accessibility Feedback' form which allows Learners to register feedback directly with the edx. Courses created by the University of Edinburgh contain an Accessibility Statement which addresses equality of access to information and servicesandis available via the 'Support' page.

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

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