Topic :  What is the future of Data Science?

A panel discussion on the landscape of data science with a variety of data science practitioners to add their experience and perspective.

Topics & Questions to be addressed:

• The most interesting case studies of applied analytics that our panelists have been involved with
• Is it all machine learning, AI, and coding?
• Does the data have to be big? and what is “big data” anyway?
• What background is most suitable for data science?
• What is the need for statisticians? mathematicians? computer scientists?
• How essential are skills in SAS, SPSS, python, and r ?
• What are the new “must have” skills?
• Is a graduate degree a must?
• Are data science bootcamps replacing graduate degrees to get people into the field quickly?
• How is data science different than data mining? business intelligence?  analytics?
• How does/should data science fit into an organization?

We will also hear from our panelists their advice for those considering a career in the field.

Panelists

Neil Bartlett, SVP Enterprise Information Management at RBC

Ceni Babaoglu, Senior Data Analytics Associate at Ryerson University

Emma Warrillow, Chief DiGGer, Data Insight Group Inc.

Ozge Yeloglu, Chief Data Scientist, Customer Success Unit at Microsoft Canada

Sarah Siu, Business Intelligence, Shopify

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DESCRIPTION

This talk will give an overview of Artificial Intelligence from a practitioner who has been a leader in the analytics world for many years. The talk will cover the following:

1. Provide a definition of AI. A term that is very misunderstood and misused in the market place.
2. Discuss the missing elements in the current conversation about AI that are required to delivered applied solutions.
3. Provide Retail and Finance Industry AI use cases that Daisy is delivering to clients today.
4. Share case study results for Daisy use cases.

About Gary:

Gary is an expert on AI technology and evangelizes about the societal benefits of AI and machine learning as it pertains to improving business decision making. Gary’s love of math and science started with punch cards and parallel computing meant driving back and forth to computers at all his friend’s houses to run jobs to get his computational aerospace assignments completed.

Gary is the former head of IBM Canada’s analytics and data warehousing practices and previously the head of Loyalty Consulting Group, providing analytical and data management services for one of the world’s most successful coalition loyalty programs. Gary holds both a BASc and MASc in aerospace engineering from the University of Toronto and continues to lecture and advise in curriculum development for the Engineering Science and AI programs at U of T.

Gary is the founder and CEO of Daisy Intelligence which has a proven record-of-success applying A.I. powered technology to automated core business decisions. Daisy’s software-as-a-service (SaaS) platform analyzes 100% of input data, simulates the possible alternatives and trade-offs inherent in any complex business question, considers any boundary conditions and constraints, and recommends the optimal course of action to best meet a client’s goals.

For more information visit SORA-TABA-DLSPH Workshop 2018.

The role of geography in data integration and predictive analytics 

 Tony Lea, Ph.D. Senior VP and Chief Methodologist, Environics Analytics

In a world where data governance and privacy concerns make it increasingly unable to use data on individuals it is important to have an alternative that works. This paper explains how the use of aggregated data for small scale geography can provide much of the solution in a privacy friendly way. This is called geodemography. After working through some theory, an example is presented that shows how spatial autocorrelation and segmentation can facilitate effective data integration and powerful predictive models. This approach allows users to have thousands of potential predictive variables – socioeconomic and demographic, behavioural, psychographic etc. – beyond those available from customer transactional and tombstone files Not only does this provide better targeting tools but equally important the ability to generate effective marketing communication strategies.

Building reliable machine learning (ML) models for use in production-level systems present specific risk factors not commonly addressed in the practitioners’ literature. As ML continues to play a central role in decision making processes, it is critical to evaluate models under several conditions to identify potential defects. In this session, we introduce model testing and validation methods to ensure the production-readiness of a model. In particular, we discuss concepts such as statistical model criticism, performance uncertainty, model staleness, calibration, algorithmic bias, and interpretability. The underlying methods to tackle these issues are essential for the long-term health of ML production systems, and should be seamlessly integrated in data science pipelines.

See video here https://youtu.be/7YEwoasDVbI