Data Science and Analytics Training and Engagement Services for Business – HEIF project
We are experiencing an explosive growth of digital content. According to International Data Corporation, there are currently over 2.7 zetabytes of data. It is estimated that in 2020, the digital universe will be 50 times as big as in 2010 and that from now until 2020 it will double every two years.
The commercial world has been transformed by Big Data with companies competing on analytics. Data has become a commodity referred to as the ‘new oil’. We are entering a new era of predictive analytics and data intensive computing which has been recognised worldwide with various high profile reports. In a recent UK-wide report commissioned by SAS UK (one of our key industrial partners) it has been estimated that there will be about 132,000 big data job opportunities created in the UK economy between 2012 and 2017. McKinsey’s report states that by 2018 the US alone will face a shortage of between 140,000 to 190,000 people with deep analytical skills, while in the UK such shortage will be in the region of 58,000 (e-Skills UK5). Another SAS commissioned report focusing on “data equity” and its impact on the UK, states that increasing adoption of big data analytics will result in cumulative benefits of £216 billion over the years 2012-17.
Following the success of recently launched MSc in Applied Data Analytics, this HEIF project seeks to take advantage of a large demand for and addresses the widening advanced analytics skills gap. Our HEIF project focuses on:
- Engagement with industry through a provision of an on-going opportunity for contact, information and advice in the Data Science Surgeries which are open to businesses of all sizes as well as university staff and students. This service is to support the creation of Knowledge Exchange professional network in the Data Science and Analytics area helping to identify potential skillset needed as well as transfer of knowledge and collaborative research opportunities.
- Development of a portfolio of CPD/short courses within an area with acute UK-wide shortage of skills and where, within the Data Science community consisting of over 50 academics from four faculties, BU has a wealth of expertise and excellent track record.
Over time, the Data Science Surgeries and CPD courses will facilitate engagement between industry and the broader BU Data Science community, enabling us to build bridges and develop relationships with industry, as well as interdisciplinary research collaborations. The new perspectives developed through this interdisciplinary collaboration will not only help to give a better understanding of some of the complex problems facing our society, but also help to inform both the teaching and professional practice undertaken by our academics -supporting the vision of Fusion at BU.
I am delighted to share with you good news about the small grant that I was awarded from the Grants Academy at BU. It will help me to develop my research in the area of predictive analysis of complex networks.
The main goal of the project that will be a result of this small grant is to develop a robust and adaptive framework for Predictive Analysis of Complex Social Networks. Sound mysterious and you are probably asking so what?
Let me give you some background. For the first time in history, we have the possibility to process big social data about the interactions and activities of millions of individuals that can be represented as a social network. It represents an increasingly important resource yet is underutilised due to the scale, complexity and dynamics of these structures which makes them extremely difficult to model and analyse. As only recently the development of ICT technology has made collecting this data feasible, there is no coherent and comprehensive approach to analyse such networks and their dynamics which is crucial to advance our understanding of continuously changing people’s behaviour. It means that we need new approaches that will enable us to analyse and predict the future of social networks.
And now the next question that you are asking is probably: so why is it important?
Again, let me give you some examples that shed a little bit of light onto importance of my study:
a) Improving national security
The outcomes of this project will be applied in a collaborative research, with a visiting researcher, Prof. De Meo from the University of Messina, Italy, focused on Cosa Nostra analytics, for understanding of the organisation of Mafia syndicates. Application of my framework for Predictive Analysis of Complex Social Networks will contribute to the improvement of well-being and security of citizens. Results of this joint cross-disciplinary research will help law enforcement agencies and policy makers to more efficiently allocate resources in the fight against Mafia
b) Improving health and well-being
The results will also be applied in the cross-disciplinary collaborative research with a visiting researcher, Dr De Ruddere from the Ghent University, Belgium, to understand the social exclusion of patients with chronic pain. Application of my framework will facilitate the understanding of how the social networks of people with chronic pain evolve over time contributing to the improvement of the patients’ quality of life and social well-being.
If you would like to have a chat and hear more about my research please keep in touch: email@example.com
Katarzyna Musial-Gabrys was invited to present her work on complex social networks during the upcoming workshop organised by the Alan Turing Institute within the Foundation of Social Data Science initiative.
The Alan Turing Institute was established in 2015 as the UK national institute for the data sciences in response to a letter from the Council for Science and Technology (CST) to the UK Prime Minister (7 June 2013), describing the “Age of Algorithms”. The letter presents a case that “The Government, working with the universities and industry, should create a National Centre to promote advanced research and translational work in algorithms and the application of data science.” (https://www.gov.uk/government/publications/the-age-of-algorithms).
Katarzyna’s presentation will contribute to shaping the portfolio of research challenges to be addressed within the Alan Turing Institute.
Title of Katarzyna’s talk: Methodological challenges in data aggregation in complex social networks.
Abstract of the talk:
For the first time in history, we have the possibility to process ‘big data’ (gathered in computer systems) about the interactions and activities of millions of individuals. It represents an increasingly important yet underutilized resource because due to the scale, complexity and dynamics, social networks extracted from this data are extremely difficult to analyse. There is no coherent and comprehensive methodological approach to analyse such networks which is crucial to advance our understanding of continuously changing people’s behaviour.
One of the methodological challenges is to cope with the variety of available big social data. This data comes from multiple systems (email, instant messengers, blogs, social networking sites, google searches, YouTube, etc.); in each system user can have one or more accounts; this data describes different types of activities (commenting, sharing, messaging, calling, etc.) and relationships (direct, quasi-direct and indirect). In order to be able to effectively process gathered data using data science approaches we need to develop new methodology that will focus on the multirelational (more than one type of connections in a network) character of data.
In general, there are two methods to do that: (i) analyse each relation type separately and then combine results from different layers or (ii) merge all relation types in one layer and analyse this newly created layer. Both approaches require effort in terms of redefining existing network analysis techniques. Analysing each network separately means that methods for combining results from different layers need to be developed. Merging some/all connection types into one heterogeneous relation means that a new approach for aggregation of data from different layers is required. Only by developing rigid approaches to data aggregation, the analytics task can be performed.
If you are interested and you would like to get some further information please contact firstname.lastname@example.org.
Over a month has passed since I re-joined Bournemouth University. As some of you may remember, I first joined BU in 2010 but then went to King’s College London for almost four years. Now I am back in my new role of Principal Academic in Computing (what a mystery that job title is!). Living at the sea side cannot be overrated!
Main areas of my research are complex networked systems, and analysis of their dynamics and evolution, as well as predictive, adaptive modelling of networked systems. I have recently started research in a new direction – the application of machine learning approaches to networked, dynamical systems. So, if you have some data for analysis, please keep in touch.
As for my experience, I received my MSc in Computer Science from Wroclaw University of Technology (WrUT), Poland, and an MSc in Software Engineering from the Blekinge Institute of Technology, Sweden, both in 2006. I was awarded my PhD in November 2009 from WrUT, and in the same year I was appointed a Senior Visiting Research Fellow at Bournemouth University (BU), where from 2010 I was a Lecturer in Informatics. I joined King’s College London in November 2011 as a Lecturer in Computer Science and I worked there till the end of August 2015. At Bournemouth I work in the Faculty of Science and Technology and together with my colleagues we try to develop Data Science Institute that is a cross-disciplinary initiative at BU.
I hope that I will be able to meet you in the future, maybe over a cup of coffee? Please do let me know if you think that my research work may be relevant to what you do.