On the 6th of March 2020, the 360 Degrees of Data Science symposium will be held for the first time. This free one-day symposium will showcase applications of data science in novel domains.
The keynote of the day will be given by Stefania Giodini of the Red Cross on data science for disaster response and Janina Rannikko of the Helsinki XR Center.
In addition to guest speakers, there will be various interactive workshops with case studies from industry and a panel on the ethics of applying data science in the real world. Female data scientists will take a central stage on this day to demonstrate how data science can be an interesting career choice not only for men but also for many women.
Whether you are a novice interested in learning more about data science or an experienced data scientist intrigued by how NGOs and companies are using data, this symposium will provide an interesting peek into how data science can benefit many fields, also those you might not have expected.
See you in Leiden on the 6th of March!
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Short presentations on new directions in data science by Lauren Fontyn (Avoiding anachronisms: data science for historical linguistics), Anne Dijkstra (A machine learning approach to estimate sex in human skeletal remains), and Irene Martorelli (Data Science for fungal biodiversity).
Panel members: Catholijn Jonker (TU Delft), Karin Jongsma (UMC Utrecht), Aysenur Bilgin (VIQTOR DAVIS/CWI), Martijn van Otterlo (OU/RU Nijmegen)
Dr. Giodini leads the operations of 510.global, an initiative of the Netherlands Red Cross to use data science to positively impact faster and more (cost) effective humanitarian aid. They aim to help aid workers, decision-makers and people affected by disaster by converting data into understanding. Dr. Giodini received her PhD in Astrophysics at the Max Planck Institute for Extraterrestrial Physics in 2010. Before working at the Red Cross, she worked at TNO as a System Engineer and Technical Project leader in the field of Autonomous Systems.
What is a team of geeks doing at the Red Cross? Here comes data science for humanitarian aid When responding to a disaster humanitarian workers wish to have data at hand to understand the context and vulnerabilities of the population affected, pin point vulnerable groups, understand the extent of the damage and use these data to plan the operations. Even better aid workers would like to be able to forecast the disaster and prepare the people affected to mitigate its impact. The 510 data team at the Netherlands Red Cross is contributing making this wish a reality. The 510 data team was born in 2016 at the Netherlands Red Cross headquarters with the mission to shape the future of humanitarian aid by converting data into understanding, put it in the hands of aid workers, decision-makers and people affected, so that they can better prepare for and cope with disasters and crises. In this talk you will see showcases from our data science applications and how they are being applied in Red Cross operations ( e.g automated damage assessment, impact based forecasting and community risk assessment) as well as how we keep an eye out for data responsibility and data ethics.
Dr. Rannikko has a PhD in paleobiology and is working as a XR data curator for the center of extended reality in Finland. Her PhD research focused on using data science for understanding evolutionary processes. By analyzing 3D scanned teeth of ancient pigs she and her colleagues have been working on methods and perspectives for interpreting the global fossil record, reconstructing past environments, vegetation and climate change.
The Importance of Data Science in Modern Paleobiology For many the heart of palaeontology is the fieldwork. The adventures in remote places on Earth and the thrill to do completely new discoveries beneath the ground. Or see the magnificent bone collections in the museum basements. I dreamed of those things when I was a kid. I wanted to be a palaeontologist and dig up dinosaurs. Now, as a fully trained scholar in science, I value different things: the amount of fossil specimens and data that palaeontologist and others have collected during the history of natural sciences. I identify myself as a palaeobiologist, someone who studies the biological world of the past. The past world cannot be observed like the modern world. However, data can be gathered from various fossil specimens and species sheets that fill museums and other collections. Moreover, if one knows how to use and analyse that data, ancient worlds can be studied as effectively as our modern world. This talk will take you from the heart of palaeontology to the use of modern data science in the study of ancient worlds. The ancient environments can be studied via species composition and morphological characteristics. Different kinds of animals are adapted to different kinds of ecological niches. The knowledge gathered from present-day animals can be used to interpret the ecology of the ancient animals and conditions of their surrounding environments. The key is to understand and explain real phenomena that cannot be observed, with data that can be obtained.
RTL, the largest commercial broadcaster in a declining Dutch TV market, is making a transition from a traditional TV company to a consumer-focused media company. A team of a dozen data scientists delivering data-powered products across RTL aimed at helping users find the right content for them, ranging from the 1M daily visitors on the RTL news website to the over 2B yearly video plays, most of these on our rapidly growing video-on-demand platform Videoland. During this workshop, participants will explore how data science can be used to make news more engaging and personal using local news articles. Bring your laptop!
At Pacmed we collaborate with healthcare providers to develop decision support tools based on the analysis of routine healthcare data. We combine machine learning with medical expertise to learn from large volumes of data. The responsible implementation of machine learning tools is at the core of our doing to ensure the patients’ safety.To responsibly implement machine learning tools in practice, we face several challenges that range from technical challenges such as explainability, transparency, uncertainty and fairness, up to non-technical concerns like ethics, privacy and liability. In this workshop we will together explore what components should be considered to responsibly implement machine learning in practice.
Outsiders as well as insiders are interested in how the government is spending its money. Are purchases made sustainably; are contracts given to small and medium sized companies or multi-nationals and: how accurate are the expected costs in the offers? Information about what is being tendered, ordered and bought is logged in several systems within the administration. In this workshop, we are going to find out how we can extract data from these systems to make information on how the government is purchasing goods and services available to interested parties. An example of the data that we will be looking at can be found here.
How can text mining be used to enhance the customer experience? Picnic is one of the fastest growing online supermarkets in Europe that strives to fulfill their customers' wishes and learn from customer feedback. In this workshop, you will first follow a presentation on how to extract valuable business information from text data. In the second half of the workshop, you will do a guided tutorial on sentiment analysis. Bring your laptop!