PPT-Analytics for a Smarter Government: Business Intelligence & the Future of Predictive

NIH Project Management Community Meeting November 9 2011 Contact Information Jon Desenberg Policy Director The Performance Institute 805 15 th Street NW 3 rd Floor Washington DC 2005. Though many people take it for granted every day the world relies on a complex network of transportation systems to move people food and the products that sustain civilization Airplanes trains ships cars buses trucks and bicycles navigate myriad rou Luc Janssen. Director, Product Management, QAD Inc.. QAD Explore 2012. 2. The following is intended to outline QAD’s general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, functional capabilities, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functional capabilities described for QAD’s products remains at the sole discretion of QAD.. Seayoung. Rhee. Sr. Technical Product Manager. Microsoft Advanced Analytics. BRK2569. Transformational . trends. cloud . computing. 2011 .  2016 5x increase. e. merging data science talent. Universities filling . San Francisco. , February 19, 2009. The Unrealized Power of Data. Andreas Weigend. people & data. Outline. Q: Current bottleneck for you in your business? (Scarce . vs. abundant)?. Historical perspective. Brian Z. Brown, FCAS, MAAA. Principal and Consulting Actuary. Stan Smith. Predictive Analytics Consultant . April 29, 2016. Advancements In Reserving. Use of stochastic methods.. . Advancements in computing power have allowed for more sophisticated reserving methodologies. David M. Levine, Baruch College—CUNY. Kathryn A. Szabat, La Salle University. David F. Stephan, Two Bridges Instructional Technology. analytics.davidlevinestatistics.com. DSI . MSMESB session, November 16, 2013. December 2013, Jakub Miarka, University of Leeds. RapidMiner. Formerly called . YALE. (Yet Another Language Environment). Environment for . machine learning, data and text mining, predictive and business analytics. Class 5. Tony Cox. tcoxdenver@aol.com. . University of Colorado at Denver. Course web site: . http://cox-associates.com/6330/. . What is a predictive model?. “The probability that X will happen is p” is a predictive model. is the use of:. data, . information technology, . statistical analysis, . quantitative methods, and . mathematical or computer-based models . to help managers gain improved insight about their business operations and . Information Systems Development. Learning Objectives. Upon successful completion of this chapter, you will be able to:. Explain the difference between BI, Analytics, Data Marts and Big Data.. Define the characteristics of data for good decision making.. Tanya Scott. Director, Business Analytics Department. Assoc. Dean, Business & Public Services . Dr. Manju Shah. Assoc. Department Head, Business Analytics Department. Wake Technical Community College . Presented by:. Tanya Scott. , Director, Business Analytics Department & Associate Dean, BPST. Wake Technical Community College. Social Media!. Twitter:. @. LearnAnalytics. AAS Business Analytics:. Dr. Sagar . Samtani. Assistant Professor and Grant Thornton Scholar. Kelley School of Business, Indiana University. 1. Bootcamp Background – AI-enabled Analytics. Artificial Intelligence (AI) has rapidly emerged as a key disruptive technology of... Tanya Scott. Director, Business Analytics Department. Assoc. Dean, Business & Public Services . Dr. Manju Shah. Assoc. Department Head, Business Analytics Department. Wake Technical Community College .