Edmonton Data Science meetup is a place to learn about data science and network with fellow Data Scientists. The presentations for the upcoming meetup include:
• "Data Driven Decision Making and Workforce Management in Healthcare" - Omer Shah (Alberta Health (http://www.health.alberta.ca/))
• "Redis for Big Data" - Kyle Davis (Redis Labs (https://redislabs.com/))
3rd Annual Data Analytics, Machine Learning and Data Management Conference at NAIT Oct 27-29, 2017 [details (https://drive.google.com/file/d/0B1W2-cFX-k14TllHb2Mwd3lxSFE/), website (http://dmc-conf.com/index.html)]
Pizza will be provided before presentations and after presentations we will head to a pub for free beer and networking!
The meetup is sponsored by DAMA (http://dama-edmonton.org/index.html) and Granify (http://www.granify.com/).
Edmonton Data Science (EDS) meetups are a platform for data science enthusiasts and professionals to:
• learn about big data techniques and tools
• discuss best practices in big data and data science
• promote big data and data science among university students and the local tech community
• learn about practical applications of data science methods
• learn about companies that use data science as the foundation of their business processes
We are passionate about data science and big data. We hope to create a vibrant data science community that is engaged in sharing and learning different topics in the field.
In these meetups, we intend to a) share best practices and knowledge of data science-based startups and corporations with university students, b) introduce research works on big data and data science to the local tech community. We aim to connect the students to companies and make them more aware of our vibrant local tech community. We also invite professors and researchers to present their work on data mining and machine learning methods to the professional community. Each meetup will include a presentation introducing a company who is using data science techniques, and two presentations about big data, data science and machine learning techniques.