Join our community of hundreds of researchers, analysts and data scientists for an opportunity to network, develop new skills and gain insight into the evolving field of data science.
Hear from industry and academic speakers representing a range of sectors, from research and bioinformatics to business and finance
Learn about the practical application and implementation of the latest tools, techniques to industry case-studies.
Share knowledge, pick up new ideas and connect with developers, analysts, researchers and executives.
Brought to you by
Prof. Sunil Vadera
Professor, University of Salford
Session: Research Challenges in Applying Data Mining
Abstract: This talk outlines the future challenges that need to be addressed if Big Data Analytics is going to be successful in addressing regional and global challenges including managing energy consumption, climate change, finance, health and social inclusion.
Session: Introduction to Deep Learning and Natural Language Processing
Abstract: Using Machine Learning and vector representations of text for Natural Language Processing has been around for a long time but with recent developments in neural networks and dense representations it has become the de facto standard for many NLP tasks.
Partner, Appleyard Lees
Session: An overview of patent activity in machine learning
Abstract: We live in a world where a vast quantity of digital data is generated on a daily basis – an often-quoted statistic is that 90% of the world’s data was created in the last two years. The prevalence of these “big data” sets has opened the flood gates for the application of machine learning to solve an enormous variety of problems, from bioinformatics to FinTech. This surge in the application and sophistication of machine learning has been accompanied by a corresponding surge in patent filings over the last few years. As innovators developing better algorithms, or apply them to new problems, they seek to protect their inventions. Julia Gwilt will lead you on a tour through the machine learning patent landscape.
Head of Analytics, Hello Soda
Session: Deploying Machine Learning Models as A Service
Abstract: In this session, Leanne will take us through how Hello Soda have developed the in-house ability for data scientists to deploy their data products and models directly into a live production environment. Leveraging a modern technology and open source stack - including the use of Docker and a cloud based micro-services infrastructure - Hello Soda have enabled code agnostic model deployment such that the data scientist can be an active participant in the complete model and data product lifecycle, regardless of their preferred coding language. Leanne will explore why there was a need for such a solution, why third party solutions were not meeting the needs, and how the in-house solution has been incorporated in addition to discussing the next series of challenges faced.
Senior Manager, PwC
Graphic Exposure - How Graph Theory uncovered a major fraud
Abstract: This talk explores a real business fraud case utilising fast big data ingestion to unlock the power of deep learning and graph. The fraud case presented will reveal a lot more than just fraud. The twists and turns of the investigation will be discussed and how the fraud remained undetected despite all best efforts. At the time of the fraud deep learning and graph databases were not readily available, and the talk will demonstrate a significant reduction in time to detection using these newer techniques. It will also focus on the world of “Big Data” and how this can be utilised to gain advantage and help compliance despite the introduction of ever changing legislation and regulation.
Healthcare and Life Sciences Industry CTO, IBM
Session: Healthcare Data Analytics
Abstract: Mike Broomhead has worked with Government clients for 10 years including BI/Analytics, focusing on Healthcare and Life Sciences for the last 2 years. He will recap the drivers of 'why' health clients are looking at how to make better use of data with analytic techniques, some real-world examples from UK and Internationally, and share some experiences of tips/traps.
Dr. Pablo Suau
Senior Data Scientist, Department for Work and Pensions
Session: The importance of experiments, reproducibility and analysis automation in Data Science projects
Abstract: The success of Data Science projects depends on the ability to continuously deliver value and on being able to build upon what has been created in the past. Making sure that every member of the team not only has access to the data but also to the code and environments required to repeat any analysis makes it easier to focus on producing results. Adopting practices from the software engineering world like version control systems, code reviews, continuous integration and the deployment of automated analytical pipelines helps to increase the quality of our work, ensures that our data-based applications are always up to date and reduces the time investment required to analyse other sources of data. In this talk we will discuss the challenges faced by our data science team in the path to adopt and implement these principles in our day-by-day work.
Head of Data Science, Peak
Session: Using the 'Perfect Prediction' approach to problem formulation in Data Science
Abstract: What would you do differently if you could perfectly predict the future? What's the key bit of information that would help you, if only you could foresee it? This talk will reveal how answering these questions can help design a data science strategy in business. Systematic problem formulation is an often-overlooked element of Data Science. Many data-driven analyses provide interesting insights without giving an obvious way to exploit them. At Peak, we are often approached by businesses who are overwhelmed with data, but don't know what to do with it. Here, Tom will explain how considering what you would do if you had flawless foresight can provide a framework for solving real-world business problems using data science.
Dr. Erol-Valeriu Chioasca
Session: AI Systems for Requirements Quality and Compliance
Abstract: Using AI techniques to attain regulatory compliance and data quality is a challenging task. We first need to define what we want to achieve by looking at a few real-life examples. We then focus our attention to one of the central areas of AI - Semantic Analysis of Natural Language. By analysing specific past and present approaches we learn about available NLP techniques for our specific task. Finally, we look at the state-of-the-art as well as some ideas on what the future of Semantic Analysis might look like.
Session 1: Data Science Essentials
- Supervised vs. Unsupervised Learning
- The Numpy library for array manipulations
Session 2: Data Analysis using Python
- The Pandas library for data manipulation
- Data cleaning and pre-processing
- Data visualisation with Matplotlib
Session 3: Machine Learning Techniques
- The scikit-learn library for Machine Learning
- Applying Principal Component Analysis
Past attendees include
- Conference talks are suitable for all individuals looking to get insight into the latest data science topics, applications and key challenges faced in industry.
- Workshops will assume some basic knowledge about programming in Python. You can acquire these skills at our Python bootcamp.
Got questions? Get in touch
Our team is here to help. Get in touch to clarify any questions you have about the Data Science Summit.Contact our team