CSCDS Data Science Expo 2020

To further our objectives of creating future-ready students and building a dynamic data science community, we are introducing our first CSCDS Data Science Expo. On January 21, 2020, we will welcome speakers and panelists to share their experience and perspective in the field of data science. There will also be the opportunity for companies and organizations to showcase themselves and to connect with young and determined minds – undergraduate and graduate students, McGill Alumni, post doctoral students and other professionals.  We hope you’ll be a part of what we anticipate will become a growing yearly event to advance and connect the Montreal data science community.

About CSCDS:

The world’s problems are increasingly technical and increasingly social at the same time. Every company and organization needs to bridge the worlds of arts, humanities and data science in order to address them. The Centre for Social and Cultural Data Science (CSCDS, or “cascades”) is building that bridge. We are creating a new kind of student who can imagine and deliver innovative, relevant, and responsible uses of data science that authentically engage with the challenges governments, companies, and organizations face.

*Schedule and panel topics subject to change

CSCDS 2020 - Getting Data Protection and Policies Right - It’s Not Easy!’ with Danielle Miller Olofsson of BCF Business Law
January 21, 10:00 am -11:00 am
New Residence Hall
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Whether collected from stop light cameras, smart devices, or social media - data contains snapshots of people's everyday lives. This makes the data that companies collect sensitive and important to treat with care. A lot of well-intentioned companies get this wrong. Whether as a legal consultant, a user interface designer, a marketer, or a developer, it's important to be aware of how to treat data properly. This session will walk through several case studies and show how tricky it can be to get data collection, privacy, and protection right - and why it requires a team with a wide range of expertise - from security to public policy - to get it right.

CSCDS 2020 - From Courses to Careers’ by Derek Ruths, Director of the Centre for Social and Cultural Data Science and Associate Professor of Computer Science at McGill University
January 21, 10:15 am - 11:15 am
New Residence Hall
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Whether you're majoring in environment, communications, language, or statistics, McGill has many opportunities for students to gain skills and experiences that will position them for a career in data science. In this session, Derek Ruths – professor of Computer Science and director of the Centre for Social and Cultural Data Science – will highlight available programs, courses, and extracurricular opportunities. We'll chart paths for students in all kinds of degree programs that lead to careers in the world of data science.

CSCDS 2020 - Data Science App Development in the Real World with Kevin Phan of Plotly
January 21, 11:15 am -12:15 am
New Residence Hall
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Plotly's visualization products are used by businesses across a wide array of industries, by people in all different kinds of roles - from engineering to sales to HR. Kevin will touch on Plotly's experiences building and deploying in these environments. He will highlight the importance of considering the end user's perspective when developing data science projects: whether straightforward visualizations or complex interactive modeling tools.

CSCDS 2020 - But I’m not qualified to be a data scientist!”: How to transition from university to the data science industry with Erika Braithwaite of Precision Analytics
January 21, 11:30 am -12:30 pm
New Residence Hall
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Are you thinking about transitioning to a data science career and don’t know where to start? In this session, I’ll share my experience starting a data science company after leaving academia. As a trained epidemiologist, I had to rebrand myself as a data scientist and as the company grew, I’ve also had to hire data scientists. I’ve screened hundreds of CV’s, conducted countless interviews and spoken to many folks who have finished university and are looking to move to industry. I want to share what I’ve learned and hope to provide some tips about how write your CV, how to re-brand yourself, and how to approach companies to make the transition from the university environment to an industry job in data science as smooth as possible.

CSCDS 2020 - Geoinformatics: Data Science through a Geographic Lens’ by Grant McKenzie, Assistant Professor in the Department of Geography at McGill University
January 21, 12:30 pm - 1:30 pm
New Residence Hall
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Geoinformatics (or Geographic Information Science) is the scientific discipline that studies data structures and computational techniques to capture, represent, process, and analyze geographic information. From the early efforts of John Snow tracing the source of the London cholera outbreak in 1854, to GPS-enabled military drone strikes, and crisis mapping efforts in Puerto Rico, the ability to model and analyze geospatial information has been essential. In this talk, Dr. McKenzie will give an overview of the field and career opportunities by discussing a range of cutting edge geospatial projects in government and industry.

CSCDS 2020 - Data Science, Elections, Politics and Media’ by Aengus Bridgman of the Digital Democracy Project
January 21, 12:45 pm - 1:45 pm
New Residence Hall
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As social behaviour increasingly moves online, the amount of politically-relevant data is growing. Traditional approaches to understanding mass political participation, the media environment, and electoral politics simply cannot keep up with the wealth of information we have today. Consequently, data science is a major growth field for studying election, politics, and media. This workshop explores how data science can help journalists, academics, and the general public understand elections today.

CSCDS 2020 - How diverse disciplines advance AI solutions at Microsoft Research with Fernando Diaz of Microsoft Research
January 21, 2:00 pm - 3:00 pm
New Residence Hall
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Over the past few years, artificial intelligence (AI) has moved from an area of academic research to a ubiquitous engineering discipline. In this presentation, I will reflect on the importance of approaching AI engineering problems as a dialog between multiple disciplines. Using web search as an example, I will describe on how an interdisciplinary approach can help in defining problems, engineering solutions, and monitoring performance. I will then present the current work at Microsoft Research Montréal that studies the societal impacts of artificial intelligence using this approach.

CSCDS 2020 - Anyone can cook! The value of diverse educational backgrounds on data science teams by Jenn Marton of Data Sciences
January 21, 3:00 pm - 4:00 pm
New Residence Hall
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Data Sciences (DS) is a Montreal-based company that brings together data scientists, researchers, and digital marketing specialists in order to understand how audiences behave and predict how to best engage them. Our data sciences team comes from a variety of educational backgrounds - from psychology to computer science to genetics. This talk will highlight several scenarios where diverse educational backgrounds improved the speed and/or accuracy of data science projects by supporting thorough troubleshooting, facilitating experimental design, and improving feature selection and engineering.

CSCDS 2020 - How Social Science and Design Play a Crucial Role in Building Impactful, Usable AI' with Jason Stanley of Element AI
January 21, 3:15 pm - 4:15 pm
New Residence Hall
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When we hear talk of machine learning, most people think of models and algorithms alone. But often machine learning can't survive in the wild without a rich set of human systems wrapped around it. For example, people need to make judgements about when and where machine decisions should be automated, the quality and cadence of human intervention in machine decisions, how and when to augment the data used to train a machine in response to a changing world, and so on. Without these human systems, machine learning tools often nosedive when confronted with a world more complex and dynamic than the 'lab'. In other words, technical modelling skills will only get us so far in building AI systems. We need people from design, the social sciences, and beyond to do this well.

PARTICIPATING FAIR ORGANIZATIONS (more to come)