• Recognize the types of data available and how they can be harvested
• The power and limits of data and resulting conclusions
• The importance of questioning and assessing quality
• Anchoring the approach in a scientific and transparent framework

• What do we mean by Data Visualization?
• Great data visualization examples
• The advantages and benefits of good data visualization
• How to find the most suitable data visualization tool(s) for your company
• Open-source tools and data

The Labour Market Information Council (LMIC) is exploring ways to create an open data repository for labour market information in Canada. In addition to bringing together a wide variety of data, all information will be organized and structured in ways that support the career, education, training and workplace decisions of Canadians. To do so, we have engaged Canadians in a series of public opinion research studies and focus groups about their labour market information needs.

• LMIC is assessing the feasibility and utility of developing an open, online repository of labour market data, the “LMIC Data Hub”.
• The LMIC Data Hub is meant to be a state-of-the-art well-structured, fully-scalable, relational database storing labour market information and related data.
• The LMIC Data Hub would be built around the following principles: Open Data, Open Code, Free Use, Clear Documentation, Easy to Navigate, Validated Data, and Up to Date information.
• The LMIC Data Hub would support policy makers, researchers and labour market intermediaries such as career development practitioners, educators among others who directly support Canadians make informed career and training decisions.

The Data Science Division  at Statistics Canada aims to build Data Science capacity by solving concrete problems, has an applied AI/ML solutions expertise and delivers practical results enabling clients to move forward confidently with big and unstructured data.

• Our mission and big data projects
• From intake to production, workflow and lessons learned
• Sharing through Communities of Practice
• An applied perspective: In-season crop prediction from satellite images
• An applied perspective: Retail scanner data project

• Predicting the future impact on Ottawa’s new light rail was a challenge when no timetables or the usual commuter tools existed. He will take us 5 years from now to when Ottawa has an integrated light rail system covering the entire city.
• Martin will demonstrate how to create an amazing story through the use of advanced analytical and visualization tools.
• Has your commute improved? Where should you be buying a future home? Is cycling a better commuting method? Did the city spend our money wisely?
• First he will outline the key components of a compelling data story and what were the drivers behind the analysis. Then he will show you behind the curtain on how the story was created. Using Google API’s, TomTom data, Alteryx, Tableau, Mapbox and more he will explain the challenges involved in creating true business or citizen insights!

This presentation will focus on the skill sets and tools required for the Citizen Data Scientist — a new breed of advanced business analysts. You will see how these tools help organizations apply more sophisticated data analysis when advanced data science skill sets are not available.

• In some cases, technically advanced business analysts are able to leverage these tools to analyze available big data sources without reliance on IT or highly skilled Data Scientists.
• Data Scientists are still required for complex ML problems, however it is not feasible to deploy such individuals everywhere that AI and ML will be needed.
• Skilled analysts are stepping up into a role where they can apply AI and ML techniques, with the help of user-friendly tools to get real results.