Education for Business Activities

Guest Lectures

Our business partners will be invited to speak to our students about the challenges that they have faced in employing data science technology and the solutions that they developed, confirming their reputation as an advanced user of data science.

Work Placement Hosts

The Data Science Institute has collaborated with many leading businesses to host students on work placements. Several of our students have gone on to work for the companies they did their work placements with.

Browse companies

Case Studies

The Co-operative Insurance

The Co-operative Insurance, based in Manchester, is a general insurance company. They offer a range of home, motor and pet insurance. In 2006 The Co-operative Insurance became the UK's first insurer to launch an Eco-Motor Insurance policy, offering customer discounts on ‘greener’ cars and the opportunity to offset the environmental impact of their driving. In 2011, they became the first major insurer to introduce a telematics product to help lower the cost of insurance for young drivers. The Co-operative Insurance now forms part of The Co-operative Group’s family of businesses.

The Challenge

During 2014, a Co-operative Member Score was developed for its current and future Motor Insurance customers. This is a unique customer-level scorecard derived from the Co-operative Group data, comprising 8 million members. It is being introduced as a live pricing tool to deliver a better Motor Insurance proposition for these members in 2015. Developing it requires an extensive analytic resource, investigating potential new data variables from the extensive Membership database.

Expertise Sought

  • Proficiency in Structured Query Language (SQL) and Statistical Analysis System (SAS)
  • Ability to analyse large sets of data efficiently
  • Strong mathematics ability

The Solution

Haiqin Zhuang, MSc Data Science, is analysing group membership data to identify and create new variables which help predict customer behaviour.  He is investigating trends in conversion and cancellation rates and working closely with the Home and Motor Pricing teams, using Microsoft SQL Server and SAS to analyse the data and Towers Watson Emblem, a generalised linear modelling programme, to identify trends and produce models. He is also helping to identify predictive variables and produce a range of bespoke variables that will be used to improve the membership scorecard.

Cost

The project was fully funded by The Co-operative at a total of £3,000. It is being conducted as part of the MSc Data Science at Lancaster University.

Impact

The project will aid the development of new customer groupings and variables and enabled further analysis of insurance behaviour observed within these groups. This will allow The Co-operative to set prices accordingly. It will also enhance the Co-operative group member scorecard used for pricing of personal lines insurance.

Benefits to the company

  • Potential to enhance the Co-operative’s group member scorecard used for pricing of personal lines insurance
  • Potential to save management time
  • Is making use of the knowledge and expertise of Lancaster University Data Science students

Benefits to the university

  • Increasing the University’s knowledge of group customer data and pricing methods
  • Provides the University with a new partner for its MSc Data Science

Benefits to the student

  • Gain familiarity with how data science is used within a commercial organisation and what can be achieved using analytics
  • Enhance skills in SAS and SQL
  • Enhance future job prospects in Data Science field

Company Feedback

 “We were really impressed. You are clearly teaching the students very relevant skills,” Ben Wilson, Pricing Development Actuary, Co-operative Insurance.

Student Feedback

“I will be spending three months with The Co-operative Insurance company in Manchester. I will be working on developing and testing models within their analytics team and will be able to see the results of my work applied to their business. I’m hoping to gain familiarity with how data science is used within a commercial organisation and what can be achieved using analytics. I’m looking to enhance my skills in SAS and SQL and, hopefully, be well-placed to find a good job in Data Science afterwards,” Haiqin Zhuang, MSc Data Science.

Framed Data Science Inc.

Framed Data, Inc., based in Silicon Valley, San Francisco, is a data science as a service product that helps companies understand their users and improve their business. Rather than focusing on usage analytics, Framed provides predictive machine learning products to identify potential high-value users before they buy and high-risk users before they actually leave.

The Challenge

Framed Data have built an automated machine learning platform that takes in user data and predicts when users are going to leave an application. It does this by engineering a feature space out of past user behaviour and then running a list of selection heuristics to pare down space. Churn prediction allows a company to determine which of their customers are likely to leave and take steps to prevent this.

Expertise Sought

  • Experience in programming language R and machine learning
  • Experience and ability in feature selection
  • Experience and ability in the engineering of high-dimensional datasets
  • Familiarity with statistics and data visualization
  • Familiarity with Git software and GitHub hosting service, or a version control system

The Solution

Philip Spanoudes, MSc Data Science, is working with the company’s CEO and data scientists to improve feature selection and engineering heuristics for the company’s predictive analytics pipeline. They are working together on optimising model accuracy against a subset of sample data. Models that they are creating for this subset will be run against Framed’s production data to evaluate this accuracy. Philip will try to find efficient and effective ways of both enhancing Framed’s churn prediction process and investigating the application of deep learning techniques to their analysis of churn prediction.

Cost

The project was fully funded by Framed Data at a total of £3,000. It is being conducted as part of the MSc Data Science at Lancaster University.

Impact

The research project, as part of a larger project, is helping improve the efficiency of Framed’s churn prediction, delivering an improved approach to feature engineering and selection on high-dimensional datasets at scale as well as improving the feature selection and engineering heuristics for the company’s predictive analytics pipeline. This provides the potential for Framed’s clients to better identify where and why their customers are leaving, and how to prevent them doing so.

Benefits to the company

  • Potential to increase the efficiency of Framed’s churn prediction processes
  • Potential to deliver an improved approach to feature engineering and selection on high-dimensional datasets at scale
  • Potential to improve the feature selection and engineering heuristics for the company’s predictive analytics pipeline

Benefits to the university

  • The project is increasing the university’s knowledge of churn prediction systems and how deep learning techniques can be applied to them
  • Provides the University with a new partner for its MSc Data Science

Benefits to society

  • Develop an understanding of deep learning techniques
  • Enhance technical skills
  • Hands on experience with Silicon Valley company

Company Feedback

“The calibre of students that we have interviewed at Lancaster has been top notch, and we continue to be impressed not only by their intellect but also their ability to apply their skills to industry-grade problems, especially in a fast-paced environment like Silicon Valley.”

“We hope to establish a high-quality hiring pipeline of data science candidates from Lancaster University, and Philip is clearly no exception—he has been a great team player on the data team.” Thomson Nguyen, CEO, Framed Data, Inc.

Student Feedback

 “I will be working with Framed Data Inc. investigating the application of deep learning techniques to their analysis of churn prediction. Churn prediction allows a company to determine which of their customers are likely to leave and take steps to prevent this – my project is aimed at finding efficient and effective ways of enhancing this process. I’ll spend most of my time in Lancaster, but will be going to work with Framed Data in San Francisco for a couple of weeks during the summer.

“I’m really excited to have the opportunity to work with cutting-edge data science with Silicon Valley. I’m looking to develop my understanding of deep learning, to deliver real innovation to Framed Data and to enhance both my skills and CV with a great project,”  Philip Spanoudes, MSc Data Science.

If you would like to apply for one of our Data Science Masters degrees, you need to use Lancaster University's My Applications website.