统计预测建模及其应用

Statistical Predictive Modelling and Applications

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

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爱丁堡大学
edX
  • 完成时间大约为 6
  • 高级
  • 英语
注:因开课平台的各种因素变化,以上开课日期仅供参考

你将学到什么

Discover how predictive models influence real-world business scenarios

Translate business challenges into predictive modeling solutions

Develop experience with implementing theoretic models in Python

课程概况

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.

课程大纲

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

预备知识

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.

常见问题

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.

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