09:25 - 09:30 Registration

09:30 - 10:00 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.

10:00 - 11:00 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
  

11:00 - 11:15 Morning Break

11:15 - 12:00 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
  

12:00 - 13:00 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)
  

13:00 - 13:45 Lunch

13:45 - 14:00 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
  

14:00 - 15:00 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
  

15:00 - 15:15 Afternoon Break

15:15 - 16:00 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
  

16:00 - 16:15 Feedback, Evaluation & Close