Topic: Maternal and Child Health Gains in Afghanistan in the Post-Taliban Era: A Synthesis of Big Data (Presenter: Nadia Akseer)
You are invited to join our statistics seminar next week on Oct 15 at 2pm (Nross 638). The speaker is Dr. Yinglin Qin from University of Waterloo.
Here is the abstract. Hope to see you there.
Title:� Testing the order of a population spectral distribution for
high-dimensional data
�Abstract:
Yingli Qin
Large covariance matrices play a fundamental role in various high-dimensional statistics. Investigating the limiting behavior of
�the eigenvalues can reveal informative structures of large covariance
�matrices, which is particularly important in high-dimensional
�principal component analysis and covariance matrix estimation. In this
paper, we propose a framework to test the number of distinct
population eigenvalues for large covariance matrices, i.e. the order
of a Population Spectral Distribution. The limiting distribution of
our test statistic for a Population Spectral Distribution of order 2 is
developed along with its (N,p) consistency. We will also report
some simulation results and real data analysis.
Topic: Statistics in the Wild – collecting and communicating data across cultures
Presenter: Heather Krause MSc PStat, CEO Datassist
For more information visit SORA-TABA-DLSPH Workshop 2017.
The Annual General Meeting of the Southern Ontario Regional Association of the Statistical Society of Canada will take place on May 5, 2017 at approximately 5 pm, following the SORA-TABA-DLSPH Workshop.
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.