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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 technique, you can use your data to mitigate against potential risk, identify areas of improvement and enhance the overall performance of your organisation.

Our Fundamentals of Predictive Modelling course is designed to give those not familiar with predictive modelling, key methods and ways of thinking that will prepare you for using predictive modelling effectively within your organisation.

Use the free R software to 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).

Delegates will be asked to download the latest version of R.

Unlocking the Power of Virtual

Our virtual courses have been designed with you in mind. From group exercises in breakout rooms to live chat, whiteboards and interactive polls, we use a range of tools and techniques to ensure that you can connect with your trainer; network and share best practice with your peers and leave the day with the skills you need.

Our courses provide you with an interactive and engaging learning environment that can be accessed from any location, helping you to continue to connect, learn and grow. Click here to discover more!

Please note we will use Zoom to virtually deliver this course.

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Robert Grant
Trainer, Coach and Writer on Statistics

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 ...

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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
All the Understanding ModernGov courses are Continuing Professional Development (CPD) certified, with signed certificates available upon request for event.

Enquire About In-House Training

To speak to someone about a bespoke training programme, please contact us:
0800 542 9414
[email protected]

Agenda

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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

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 - 14:00

Lunch

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

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

Round Up and Key Takeaways