Postgraduate Course: Predictive Analytics using Python - Final Project (CMSE11445)
Course Outline
School | Business School |
College | College of Arts, Humanities and Social Sciences |
Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Course type | Online Distance Learning |
Availability | Part-year visiting students only |
SCQF Credits | 30 |
ECTS Credits | 15 |
Summary | In this 30 SCQF credit course project students have the opportunity to demonstrate the ability to operationalise techniques in predictive analytics with classification and regression on a topic of their interest.
Topics covered include: Introduction to Predictive Analytics using Python, Successfully Evaluating Predictive Models, Statistical Predictive Modelling and Applications,Predictive Analytics using Machine Learning.
|
Course description |
In this 30 SCQF credit course project students have the opportunity to demonstrate the ability to operationalise techniques in predictive analytics with classification and regression on a topic of their interest.
Outline Content
1. Introduction to Predictive Analytics using Python
2. Successfully Evaluating Predictive Models
3. Statistical Predictive Modelling and Applications
4. Predictive Analytics using Machine Learning
Content covering the four areas listed above is made available on the edX platform. Besides videos and learning material, the platform provides assessment tools (quizzes, code checkers etc) to self-assess learning progress.
|
Entry Requirements (not applicable to Visiting Students)
Pre-requisites |
|
Co-requisites | |
Prohibited Combinations | |
Other requirements | The four edX modules completed as prior learning to the course, which require a foundation in linear algebra and programming in Python. |
Information for Visiting Students
Pre-requisites | The four edX modules completed as prior learning to the course, which require a foundation in linear algebra and programming in Python. |
High Demand Course? |
Yes |
Course Delivery Information
Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- Identify various business settings in which predictive analytics can be used.
- Show a systematic and critical understanding of the entire predictive analytics process.
- Critically assess, develop and implement predictive models and processes.
- Identify and evaluate social, cultural, global, ethical and environmental responsibilities and issues.
|
Reading List
All material will be written by the course organisers and made available on Edx in the form of four Edx modules each of which provides material associated with one of the learning outcomes below.
Furthermore, the following books can be considered for background reading:
Applied Predictive Modeling- Max Kuhn, Springer
Introduction to Statistical Learning- Daniela Witten, Gareth James, Robert Tibshirani, and Trevor Hastie, Springer
|
Additional Information
Graduate Attributes and Skills |
Analytical thinking
Information technology: numeracy and Big Data
Application of knowledge: knowledge integration and application
|
Keywords | Not entered |
Contacts
Course organiser | Dr Zexun Chen
Tel: (0131 6)50 8074
Email: Zexun.Chen@ed.ac.uk |
Course secretary | |
|
|