Predictive Analysis Learning Journey
Description
One of the most challenging aspects of public services is pressure resulting from changing demand. This makes resource allocation difficult and can result in delays for service users and impacts on budgets. Predictive analysis can help to quantify the likelihood of fluctuations in demand, offering an evidence-based approach to service design and improvement. Employing predictive analysis to help inform decisions and risk mitigation can result in a better, more cost-effective public service.
Predictive analysis is a branch of analytics that is used to make “predictions” about unknown events. Predictive analysis employs techniques from data mining, statistical modelling, machine learning, and artificial intelligence. These techniques are employed to analyse current data, in order to make predictions about the future. The pathway covers statistical modelling approaches, leading to statistical machine learning techniques (decision trees, regression, classification, clustering) in R and Python.
Learning outcomes
Learners should be able to:
- Explain the role of predictive for data driven decision making
- Demonstrate an understanding of statistical principles, statistical distributions, sampling and inference techniques
- Demonstrate analytical decision-making ability using decision trees, classification, regression and clustering techniques
- Execute predictive analysis experiments in R and Python and evaluate the performance of statistical models.
Pathway detail
Beginning with an introduction to the programming framework of choice, this learning journey progresses the learner through the fundamental concepts in data exploration and statistical analysis. This firm basis in deriving insight is complemented with courses that establish skills needed to deploy machine learning techniques in identifying trends in large datasets. The ability to evaluate the performance of these models is crucial to providing a quality analytical output. Quality assurance in predictive modelling outlines performance metrics, key fallacies and pitfalls to be aware of and some case studies of what and how AI-derived decisions have gone wrong in the past.
Prerequisites
No prior knowledge is needed to take part in this pathway.
Courses in this learning journey
This pathway can be completed using either the R or Python programming languages.
Predictive Analysis Learning journey in R
| Course name | Skill level | Duration |
| Introduction to R | Beginner | 2 days |
| Statistics in R | Beginner | 16 hours |
| Introduction to Machine Learning in R | Beginner | 3 days |
| Quality Assurance in Predictive Modelling | Beginner | 6 to 8 hours |
Predictive Analysis Learning journey in Python
| Course name | Skill level | Duration |
| Introduction to Python | Beginner | 2 days |
| Statistics in Python | Beginner | 16 hours |
| Introduction to Machine Learning in Python | Beginner | 3 days |
| Quality Assurance in Predictive Modelling | Beginner | 6 to 8 hours |