We hear a lot about Big Data, Data Science, Data Analytics and Artificial Intelligence in the news. This talk is meant to clarify these terms and provide a sense of the Data Analytics process for business owners, managers and other professionals who may be considering related projects. The talk will provide an overview of Data Science, Data Analytics and Big Data. We will explore the relationship between Data Analytics and Business Intelligence with an emphasis on required resources and best practices for success. Along the way you will learn the basics about Data Warehousing, Data Engineering, and Data Visualization and gain a sense of how Data Mining methods and Machine Learning technology work to develop predictive models. We will conclude with an examination of current trends in Data Analytics and key considerations for getting started in Data Analytics.
Participants will explore:
- The relationship between Data Analytics and Business Intelligence
- Best practices for success
- An overview of Data Warehousing, Data Engineering and Data Visualization
- How Data Mining methods and Machine Learning work to develop predictive models
- Current trends in Data Analytics
About Dr. Danny Silver
Dr. Danny Silver is the Director of the Acadia Institute for Data Analytics and a Professor in and former Director of the Jodrey School of Computer Science at Acadia University. His areas of research and application are machine learning, data science and data analytics and has published over 65 scientific papers in the areas of Transfer Learning and Lifelong Machine Learning. He was the President of the Canadian Artificial Intelligence Association (CAIAC) from 2009-11 and in 2016, he was awarded the Canadian AI’s Distinguished Service Award and made a CAIAC Fellow. Since 1993, he has worked on machine learning and data mining projects in the private and public sector providing technical consulting and project management services.
About Dr. Andy McIntyre
Dr. Andy McIntyre is a Research Scientist at the Acadia Institute for Data Analytics. After completing a Bachelor of Science degree at Mount Allison University in 2000, Andy continued his academic pursuits at Dalhousie University, enrolling in the Masters of Computer Science Program and later graduating with a Ph.D. in the field in 2007. Andy’s focus has mainly been machine learning with specific interest in evolutionary computation, a population-based meta learning technique for model building. His work has most recently included a 10-year role as senior researcher with the Network Information Management and Security group at Dalhousie, working on computer gaming and behaviour mining applications. He was a previously a postdoc with the department of Ophthalmology and Visual Science, investigating shape-based, predictive GP / imaging models and had further postdoctoral collaborations with National Research Council Canada Institute for Biodiagnostics Atlantic at the Neuroimaging Research Laboratory, developing models of functional connectivity with clustering and classification algorithms applied to large-scale, resting-state functional MRI (brain-imaging) data. Andy currently holds an adjunct faculty designation at Dalhousie and an adjunct position with the Jodrey School of Computer Science at Acadia University. Andy has assumed the role of Data Scientist at the Acadia Institute for Data Analytics (AIDA) as of October 2018. Research interests include machine learning, parallel architectures, artificial and evolutionary intelligence.