PPT-Data Analytics CMIS Short Course part II

Author : test | Published Date : 2018-03-16

Day 1 Part 4 ROC Curves Sam Buttrey December 2015 A ssessing a Classifier In data sets with very few bads the naïve model that says everyone is good is highly

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Data Analytics CMIS Short Course part II: Transcript

Day 1 Part 4 ROC Curves Sam Buttrey December 2015 A ssessing a Classifier In data sets with very few bads the naïve model that says everyone is good is highly accurate It never pays to predict bad. What is the problem?. 1- Data . is spread across different . people. Toren. and . Albert track Wellness . & Testing numbers. . Daniel . Reijer. . tracks . Global . patients.. IT . keeps data on patients demographics and . Chap 2: Data Analytics Lifecycle. Charles . Tappert. Seidenberg School of CSIS, Pace University. Data Analytics Lifecycle. Data science projects differ from BI projects. More exploratory in nature. Critical to have a project process. stay ahead of the curve. Uncover new information and value to . remain competitive. 4 . tracks. . designed to follow the . Analyst’s Journey. May 2-4, San Jose, California. Register . at . passbaconference.com . (CS40003). Dr. Debasis Samanta. Associate Professor. Department of Computer Science & Engineering. Lecture #11. Sensitivity Analysis. Topics Covered in this Presentation. Introduction. Estimation Strategies. 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. Federal Big Data Working Group Meetup. November 3, 2014. Dave Vennergrund. Director Predictive Analytics and Data Science. David.Vennergrund@salienfed.com. 571 766 2757. Salient Data Analytics Center of Excellence. Day 1 Part 3: Ensembles. Sam Buttrey. December 2015. Combining Models. Models can be combined in different ways. “Ensembles” refers specifically to combining large sets of large classifiers built with randomness applied to data or classifier. Dr. Brett M. Baker, AIG for Audit, NRC OIG. Manuel J. Mireles, Forensic Auditor, NGA OIG. Shiji S. Thomas, Forensic Accountant, NSF OIG. Analytics 101 Outline. At the end of this session you will be able to understand:. James Pick and Namchul Shin. 1. Definition of Spatial Big Data. Big Data . are “data sets that are so big they cannot be handled efficiently by common database management systems” (Dasgupta, 2013).. October 12, 2017. Welcome and Announcements - Juanita. Survey Results – Juanita. Practitioner Presentations. Associated Bank - Corporate Audit Services. Benjamin Arthur, CPA, CIA, CISA. Operations and Technology Audit Director. . Chap 11: Adv. Analytics – Tech & Tools:. In-Database Analytics. Charles . Tappert. Seidenberg School of CSIS, Pace University. Chapter Contents. 11.1 SQL Essentials. 11.1.1 Joins. 11.1.2 Set Operations. kindly visit us at www.examsdump.com. Prepare your certification exams with real time Certification Questions & Answers verified by experienced professionals! We make your certification journey easier as we provide you learning materials to help you to pass your exams from the first try. Professionally researched by Certified Trainers,our preparation materials contribute to industryshighest-99.6% pass rate among our customers. Course/Research Topics. Material derived from other sources and “Mining Massive Datasets” from:. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Fayé A. Briggs, PhD. Proposed Bachelor of Science (B.S.) in Business - Analytics Track. Paolo Catasti, PhD, MBA, CSSBB. Teaching . Assistant Professor. Statistics and Analytics. Top Analytics Employers in the Greater Richmond Area.

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