Data Science, Microsoft and You

In this session we'll explore how Microsoft uses data science and machine learning across it's entire business, from Windows and Office, to Skype and XBox. We'll look at how companies across the world use Microsoft technology for empowering their businesses in many different industries. And we'll look at data science technologies you can use yourselves, such as Azure Machine Learning and Power BI. Finally we'll discuss job opportunities for data scientists and tips on how you can be successful!

Data Science Seminar: Generic Big Data Processing for Advancing Situation Awareness and Decision-Support

The generation of heterogeneous big data sources with ever increasing volumes, velocities and veracities over the he last few years has inspired the data science and research community to address the challenge of extracting knowledge form big data. Such a wealth of generated data across the board can be intelligently exploited to advance our knowledge about our environment, public health, critical infrastructure and security. In recent years we have developed generic approaches to process such big data at multiple levels for advancing decision-support. It specifically concerns data processing with semantic harmonisation, low level fusion, analytics, knowledge modelling with high level fusion and reasoning. Such approaches will be introduced and presented in context of the TRIDEC project results on critical oil and gas industry drilling operations and also the ongoing large eVacuate project on critical crowd behaviour detection in confined spaces.

Data Science Seminar: The data science revolution in Physics and Astronomy

Abstract Heading into the 2020s, Physics and Astronomy are undergoing experimental revolutions that will reshape our picture of the fabric of the Universe. The Large Hadron Collider (LHC), the largest particle physics project in the world, produces 30 petabytes of data annually that need to be sifted through, analysed, and modelled. In astrophysics, the Large Synoptic Survey Telescope (LSST) will be taking a high-resolution image of the full sky every 3 days, leading to data rates of 30 terabytes per night over ten years. These experiments endeavour to answer the question why 96% of the content of the universe currently elude our physical understanding. Both the LHC and LSST share the 5-dimensional nature of their data, with position, energy and time being the fundamental axes. This talk will present an overview of the experiments and data that is gathered, and outlines the challenges in extracting information. Common strategies employed are very similar to industrial data! Science problems (e.g., data filtering, machine learning, statistical interpretation) and provide a seed for exchange of knowledge between academia and industry. Speaker Biography Professor Mark Sullivan Mark Sullivan is a Professor of Astrophysics in the Department of Physics and Astronomy. Mark completed his PhD at Cambridge, and following postdoctoral study in Durham, Toronto and Oxford, now leads a research group at Southampton studying dark energy using exploding stars called "type Ia supernovae". Mark has many years' experience of research that involves repeatedly imaging the night sky to track the arrival of transient objects, involving significant challenges in data handling, processing, classification and analysis.

Aplicación de técnicas de Big Data Science para la gestión de crisis

A pesar de la existencia de una multitud de investigaciones sobre el análisis de sentimiento, existen pocos trabajos que traten el tema de su implantación práctica y real y su integración con la inteligencia de negocio y big data de tal forma que dichos análisis de sentimiento estén incorporados en una arquitectura (que soporte todo el proceso desde la obtención de datos hasta su explotación con las herramientas de BI) aplicada a la gestión de la crisis. Se busca, por medio de este trabajo, investigar cómo se pueden unir los mundos de análisis (de sentimiento y crisis) y de la tecnología (todo lo relacionado con la inteligencia de negocios, minería de datos y Big Data), y crear una solución de Inteligencia de Negocios que comprenda la minería de datos y el análisis de sentimiento (basados en grandes volúmenes de datos), y que ayude a empresas y/o gobiernos con la gestión de crisis. El autor se ha puesto a estudiar formas de trabajar con grandes volúmenes de datos, lo que se conoce actualmente como Big Data Science, o la ciencia de los datos aplicada a grandes volúmenes de datos (Big Data), y unir esta tecnología con el análisis de sentimiento relacionado a una situación real (en este trabajo la situación elegida fue la del proceso de impechment de la presidenta de Brasil, Dilma Rousseff). En esta unión se han utilizado técnicas de inteligencia de negocios para la creación de cuadros de mandos, rutinas de ETC (Extracción, Transformación y Carga) de los datos así como también técnicas de minería de textos y análisis de sentimiento. El trabajo ha sido desarrollado en distintas partes y con distintas fuentes de datos (datasets) debido a las distintas pruebas de tecnología a lo largo del proyecto. Uno de los datasets más importantes del proyecto son los tweets recogidos entre los meses de diciembre de 2015 y enero de 2016. Los mensajes recogidos contenían la palabra "Dilma" en el mensaje. Todos los twittees fueron recogidos con la API de Streaming del Twitter. Es muy importante entender que lo que se publica en la red social Twitter no se puede manipular y representa la opinión de la persona o entidad que publica el mensaje. Por esto se puede decir que hacer el proceso de minería de datos con los datos del Twitter puede ser muy eficiente y verídico. En 3 de diciembre de 2015 se aceptó la petición de apertura del proceso del impechment del presidente de Brasil, Dilma Rousseff. La petición fue aceptada por el presidente de la Cámara de los Diputados, el diputado Sr. Eduardo Cunha (PMDBRJ), y de este modo se creó una expectativa sobre el sentimiento de la población y el futuro de Brasil. También se ha recogido datos de las búsquedas en Google referentes a la palabra Dilma; basado en estos datos, el objetivo es llegar a un análisis global de sentimiento (no solo basado en los twittees recogidos). Utilizando apenas dos fuentes (Twitter y búsquedas de Google) han sido extraídos muchísimos datos, pero hay muchas otras fuentes donde es posible obtener informaciones con respecto de las opiniones de las personas acerca de un tema en particular. Así, una herramienta que pueda recoger, extraer y almacenar tantos datos e ilustrar las informaciones de una manera eficaz que ayude y soporte una toma de decisión, contribuye para la gestión de crisis.

Data science

Guest lecture COMP1205, fundamentals and applications of data science

On the role of Computational Logic in Data Science: representing, learning, reasoning, and explaining knowledge

In this thesis we discuss in what ways computational logic (CL) and data science (DS) can jointly contribute to the management of knowledge within the scope of modern and future artificial intelligence (AI), and how technically-sound software technologies can be realised along the path. An agent-oriented mindset permeates the whole discussion, by stressing pivotal role of autonomous agents in exploiting both means to reach higher degrees of intelligence. Accordingly, the goals of this thesis are manifold. First, we elicit the analogies and differences among CL and DS, hence looking for possible synergies and complementarities along 4 major knowledge-related dimensions, namely representation, acquisition (a.k.a. learning), inference (a.k.a. reasoning), and explanation. In this regard, we propose a conceptual framework through which bridges these disciplines can be described and designed. We then survey the current state of the art of AI technologies, w.r.t. their capability to support bridging CL and DS in practice. After detecting lacks and opportunities, we propose the notion of logic ecosystem as the new conceptual, architectural, and technological solution supporting the incremental integration of symbolic and sub-symbolic AI. Finally, we discuss how our notion of logic ecosys- tem can be reified into actual software technology and extended towards many DS-related directions.

Applied deep learning and data science with a human-centric and data-centric approach

The term Artificial intelligence acquired a lot of baggage since its introduction and in its current incarnation is synonymous with Deep Learning. The sudden availability of data and computing resources has opened the gates to myriads of applications. Not all are created equal though, and problems might arise especially for fields not closely related to the tasks that pertain tech companies that spearheaded DL. The perspective of practitioners seems to be changing, however. Human-Centric AI emerged in the last few years as a new way of thinking DL and AI applications from the ground up, with a special attention at their relationship with humans. The goal is designing a system that can gracefully integrate in already established workflows, as in many real-world scenarios AI may not be good enough to completely replace its humans. Often this replacement may even be unneeded or undesirable. Another important perspective comes from, Andrew Ng, a DL pioneer, who recently started shifting the focus of development from “better models” towards better, and smaller, data. He defined his approach Data-Centric AI. Without downplaying the importance of pushing the state of the art in DL, we must recognize that if the goal is creating a tool for humans to use, more raw performance may not align with more utility for the final user. A Human-Centric approach is compatible with a Data-Centric one, and we find that the two overlap nicely when human expertise is used as the driving force behind data quality. This thesis documents a series of case-studies where these approaches were employed, to different extents, to guide the design and implementation of intelligent systems. We found human expertise proved crucial in improving datasets and models. The last chapter includes a slight deviation, with studies on the pandemic, still preserving the human and data centric perspective.