Overview
Predictive modelling is a statistical technique used to predict and forecast likely outcomes that impact your organisation, based on historical data.
By using the right predictive modelling techniques for your needs, you can use your data to mitigate against potential risk, identify areas of improvement and enhance the overall performance of your organisation.
Our Predictive Modelling and Analytics course is designed to give you the skills to prepare you for using predictive modelling effectively within your organisation.
Using the free R software, you’ll get hands-on experience of the decisions and coding required to launch and improve predictive models. Understand the concepts, processes and applications of predictive modelling, with a focus on a statistical (regression) and machine learning approach (decision trees and random forests).
All delegates will be asked to download the latest version of R.
Robert is a trainer, coach and writer on statistics and working in data science, especially data visualisation and Bayesian models.
He taught statistics and research methods to postgraduate clinical research students at St George’s Medical School and Kingston University (2010-2017), and contributed to many health services and biomedical research projects in this time. His freelance clients include Harvard Medical School, The Economist, and the Cabinet Office.
He is a fellow of the Royal Statistical Society and served on their statistical computing committee from 2012-16. He worked on clinical audits, analysing hospital quality and safety ...
Learning Outcomes
- Understand the concepts, processes, and applications of predictive modelling
- Explore different types of data with different relationships, and how they can be modelled
- Use the free R software to prepare data for predictive modelling and visualise the outputs
- Learn about the widely applicable methods: linear regression, logistic regression, decision trees and random forests
Agenda
Registration
Trainer’s Welcome and Clarification of Learning Objectives
In each workshop session, you will encounter a real-life public sector dataset. The trainer will guide you through the data, exploring and learning how to think critically about how to manipulate and model it.
Workshop 1 focusses on R and preparing data, workshops 2 and 3 on statistical models, workshop 4 on machine learning models, and workshop 5 on implementing a predictive model within the organisation, including time for discussion specific to attendees’ settings.
Workshop 1: Basics of R and Preparing Data for Predictive Modelling
- Using R on Windows or Mac: installation and basics of the language
- Loading and manipulating data using the Tidyverse packages for R
- Examining data visually using the ggplot2 package in R
- Proposing and justifying a predictive model
Morning Break
Workshop 2: Linear Regression
- Understand how regression models are made up of three key assumptions about the data
- Use R code to fit a linear regression model to the data
- Gain confidence in interpreting the outputs
- Visualise the model and compare it critically to the data
- Identify problems in the data that may undermine a predictive model
- Extend linear regression into more than one predictor variable
Workshop 3: LASSO and Logistic Regression
- Use the LASSO algorithm to select predictor variables from a large collection
- Extend linear regression into predicting binary variables with logistic regression using R
- Interpret and visualise the results
- Communicate risk effectively to non-technical audiences
- Understand how linear and logistic regression are specific instances of generalised linear models (GLMs)
Lunch
Reflection Session
- Trainer will review the day’s learning and the next stages of the course
- Delegates will have time to ask questions and share views with one another
Workshop 4: Decision Trees and Random Forests
- Use R to fit a decision tree to data
- Understand the choices in using decision and regression trees, such as pruning
- Compare different ways of communicating the predictions
- Extend into an averaged prediction across an ensemble of models: random forest
- Know the differences between statistical and machine learning models, and their pros and cons
Afternoon Break
Workshop 5: Embed Predictive Modelling within an Organisation
- Identify your key service delivery objectives and map these to predictive model strengths and weaknesses
- Understand how predictive models can be validated and revised on an ongoing basis
- Discuss enablers and barriers within a variety of public sector organisations
- Examine different approaches to embedding the data analytic workforce
Feedback, Evaluation & Close
Testimonials
