For more information visit SORA-TABA-DLSPH Workshop 2017.
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
Click here to Register!
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:
- Provide a definition of AI. A term that is very misunderstood and misused in the market place.
- Discuss the missing elements in the current conversation about AI that are required to delivered applied solutions.
- Provide Retail and Finance Industry AI use cases that Daisy is delivering to clients today.
- 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.
Asmita Usturge, Data Scientist | Microsoft AI
Asmita is a Data Scientist/ Cloud Solution Architect. She is a passionate data science developer and enabler of cloud technologies for AI. She works with Microsoft’s high potential customers like financial banks in Toronto to help them transform AI application on Azure cloud platform. She has 12+ years of experience in the field of computer science, statistics, machine learning and development of AI systems.
The talk:
The AI field is young compared to traditional software development. Best practices and solutions around life cycle management for these AI systems have yet to solidify. This talk will discuss challenges, best practices and technology to operationalize scalable AI models in enterprise
Click here to register for this seminar.
Click here to download the slides from the presentation.
Nataliya Portman | 360Insights.com
Nataliya received her Doctoral Degree in Applied Mathematics from the University of Waterloo in 2010, followed by postdoctoral training at the Neurological Institute in Montreal.
Following her postdoctoral assignment, she developed a novel approach to brain tissue classification in early childhood brain MRIs using modern Computer Vision pattern recognition and perceptual image quality models.
Nataliya has worked in many industries including neuroscience, biotech, the public sector, and various start-up software companies. Throughout her career, she has applied her expertise in Mathematics to develop numerous models including but not limited to machine learning algorithms, computationally efficient algorithms for model validation, and neural networks.
She is the co-inventor of “Bid-Assist”, a strategy for setting up an initial bidding amount to discourage low bidding behaviour, and “AutoVision”, a mobile app that allows automatic taking of pictures of vehicle views and damages recognized by an image classifier.
In January 2019, Nataliya took on a new role in Data Science at 360insights.com, committed to the development of predictive sales channel analytics that will help channel leaders maximize the return on investment of their channel incentive programs.
The Art and Limitations of Accurate Forecasting
Accurate sales forecasting is vital to marketing incentive program design, budget planning and strategic decision making. This seminar will focus on three business use cases:
1. Short and long-term forecasting of target KPI metrics
2. Estimation of sales lift due to promotional program choice and schedule
3. Trade customer/dealer purchasing behavior analysis and prediction of reward winners
Ms. Portman will discuss the challenges of building reliable forecasting time-series models, and how the forecast accuracy can be improved using a multi-model approach. The proposed approach involves preparation of various training datasets containing historic observations of KPIs on different time scales and factors that influence their outcome.
The task of prediction of reward winners based on their to-date and future purchasing activities involves learning of individual buying patterns and estimation of volatility/uncertainty in forecasted KPI values (e.g., volumes of products to be purchased). Knowledge of the uncertainty extent of predicted quantities will reveal individuals with a low degree of uncertainty (candidates to winners). She will show how to effectively evaluate uncertainty per individual that will lead to informed decision making on the final candidate list.