Aryeh Shatz has a MS in Applied Math with a concentration in Actuarial Science from DePaul University and a BS in Actuarial Mathematics from Worcester Polytechnic Institute. He has passed Exams P/1 and FM/2. He currently works as a Statistician at Information Logistics where he helps sports agents and maintains software to help athletes negotiate contracts. Previously he has worked as an Actuarial Assistant projecting benefits and performing analyses and has completed qualifying projects in automobile insurance modeling and reducing graffiti on public buses in London.
This lecture presented at Remote Sensing, Uncertainty Quantification and a Theory of Data Systems Workshop - Cahill Center, California Institute of Technology
This lecture presented at Remote Sensing, Uncertainty Quantification and a Theory of Data Systems Workshop - Cahill Center, California Institute of Technology
In this slide, A Linear Regression model is implemented without relying on Python’s easy-to-use sklearn library. This post aims to discuss the fundamental mathematics and statistics behind a Linear Regression model. I hope this will help us fully understand how Linear Regression works in the background.
A presentation by Glyn Jones, Welsh Government Chief Statistician, at the launch of the Administrative Data Research Centre Wales on Monday 23rd March 2015.
Comparing scientific performance across disciplines: Methodological and conce...Ludo Waltman
Presentation at the 7th International Conference on Information Technologies and Information Society (ITIS2015) in Novo Mestro, Slovenia on November 5, 2015.
Presentation about new data, methods and outputs to create knowledge for innovation policy. Presented at the OECD Blue Sky Conference, 20 September 2016.
Intervention d'Anne Catherine Rota, Spécialiste en Research Intelligence chez Elsevier au Forum du GFII 2015 : http://forum.gfii.fr/forum/les-nouvelles-mesures-de-l-influence-scientifique-l-apport-des-metriques-alternatives-au-pilotage-de-la-recherche
Linking Heterogeneous Scholarly Data Sources in an Interoperable Setting: the...Platforma Otwartej Nauki
“Open Research Data: Implications for Science and Society”, Warsaw, Poland, May 28–29, 2015, conference organized by the Open Science Platform — an initiative of the Interdisciplinary Centre for Mathematical and Computational Modelling at the University of Warsaw. pon.edu.pl @OpenSciPlatform #ORD2015
Presentation on the occasion of the 60th anniversary of the Econometric Institute at Erasmus University Rotterdam. Rotterdam, The Netherlands, May 27, 2016.
In this slide, A Linear Regression model is implemented without relying on Python’s easy-to-use sklearn library. This post aims to discuss the fundamental mathematics and statistics behind a Linear Regression model. I hope this will help us fully understand how Linear Regression works in the background.
A presentation by Glyn Jones, Welsh Government Chief Statistician, at the launch of the Administrative Data Research Centre Wales on Monday 23rd March 2015.
Comparing scientific performance across disciplines: Methodological and conce...Ludo Waltman
Presentation at the 7th International Conference on Information Technologies and Information Society (ITIS2015) in Novo Mestro, Slovenia on November 5, 2015.
Presentation about new data, methods and outputs to create knowledge for innovation policy. Presented at the OECD Blue Sky Conference, 20 September 2016.
Intervention d'Anne Catherine Rota, Spécialiste en Research Intelligence chez Elsevier au Forum du GFII 2015 : http://forum.gfii.fr/forum/les-nouvelles-mesures-de-l-influence-scientifique-l-apport-des-metriques-alternatives-au-pilotage-de-la-recherche
Linking Heterogeneous Scholarly Data Sources in an Interoperable Setting: the...Platforma Otwartej Nauki
“Open Research Data: Implications for Science and Society”, Warsaw, Poland, May 28–29, 2015, conference organized by the Open Science Platform — an initiative of the Interdisciplinary Centre for Mathematical and Computational Modelling at the University of Warsaw. pon.edu.pl @OpenSciPlatform #ORD2015
Presentation on the occasion of the 60th anniversary of the Econometric Institute at Erasmus University Rotterdam. Rotterdam, The Netherlands, May 27, 2016.
On Information Quality: Can Your Data Do The Job? (SCECR 2015 Keynote)Galit Shmueli
Slides by Galit Shmueli for keynote presentation at 2015 Statistical Challenges in eCommerce Research (SCECR) symposium, Addis Ababa, Ethiopia (www.scecr.org)
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This slides summarize the Data Analytics Club's information, including vision, mission, board members, and planned activities for the academic year of 2016 - 2017.
ow-a-days data volumes are growing rapidly in several domains. Many factors have contributed to this growth, including inter alia proliferation of observational devices, miniaturization of various sensors ,improved logging and tracking of systems, and improvements in the quality and capacity of both disk storage and networks .Analyzing such data provides insights that can be used to guide decision making. To be effective, analysis must be timely and cope with data scales. The scale of the data and the rates at which they arrive make manual inspection infeasible. As an educational management tool, predictive analytics can help and improve the quality of education by letting decision makers address critical issues such as enrollment management and curriculum Development. This paper presents an analytical study of this approach’s prospects for education planning. The goals of predictive analytics are to produce relevant information, actionable insight, better outcomes, and smarter decisions, and to predict future events by analyzing the volume, veracity, velocity, variety, value of large amounts of data and interactive exploration.
Education plays a vital role in nation’s overall development process. To be effective, analysis must be timely and cope with data scales. The scale of the data and the rates at which they arrive make manual inspection infeasible. Predictive analytics can help and improve the quality of education by analyzing the historical data of the student and allow the decision makers address factors such as increased drop-out rate, fees structure in the upcoming years, unemployment, Recommender Systems for Professional Development and curriculum Development. This paper presents an analytical study of student progress report and help to plan accordingly to achieve success.
1. Aryeh Shatz
7106 N Sheridan, Chicago, IL, 60626
(617)-959-2524
shatzari@gmail.com
Education
DePaul University, Chicago, IL
MS in Applied Math with a concentration in Actuarial Science June 2013
GPA: 3.64
Worcester Polytechnic Institute, Worcester, MA May 2009
Bachelor of Science in Actuarial Mathematics
Exams
• Successfully Completed SOA/CAS Exam P/1, Successfully Completed SOA/CAS Exam FM/2
Work Experience
Information Logistics, Skokie, IL October 2013 – Present
Statistician
• Assists sports agents with using the software product to help get players better contracts
• Helps maintain software product
• Markets software to new sports agents
• Statistical analytics to help players make more money on contracts
• Generates ideas to assist in company growth
Ogen Ltd, Tel Aviv, Israel March - September 2010
Actuarial Assistant
• Projected present value of future severance benefits due to retirement, death, disability, and
termination
• Extensive work with Microsoft Excel and the Proval Software Package in order to valuate pension
funds and perform sensitivity analysis.
• Assisted with decrement analyses projecting future mortality rates and termination rates by years of
service
• Worked on core projections in collaboration with Towers Watson
Major Qualifying Project with Hanover, Worcester, MA October 2008 – April 2009
• Worked on a team of three students in order to quantify and evaluate historical trends in Frequency,
Severity, and Pure Premium for automobile insurance. Goal was to develop a general model by
which our sponsor could accurately predict future losses.
• Strategies included testing historical data, researching loss trend methods, and fitting these methods
with historical data to test accuracy.
• Presented findings to our sponsor at Hanover once a month with final results to senior management.
Interactive Qualifying Project, London, England Winter 2008
• Worked in a team of four students to assist the Metropolitan Police of London in their efforts to
combat graffiti on public buses
• Researched motives of anti-social behavior, consulted employees and volunteers of youth outreach
programs, and interviewed youths of London.
Computer Skills
• Proficient in Matlab, SAS, R, Minitab, Proval, Excel, Access, PowerPoint, and Word