PPT-Introduction to Health Care Data Analytics

Author : ellena-manuel | Published Date : 2018-09-16

Module 4 Data Analysis Tools and Techniques Lecture a This material was developed through a collaboration between Bellevue College and the Veterans Health Administration

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Introduction to Health Care Data Analytics: Transcript

Module 4 Data Analysis Tools and Techniques Lecture a This material was developed through a collaboration between Bellevue College and the Veterans Health Administration US Department of Veterans Affairs funded in part by the Department of Health and Human Services Office of the National Coordinator for Health Information Technology award number 90WT0002. DSC340. . Mike . Pangburn. Organizations . are drowning . in . data…. … but . lacking in information / knowledge. Our ability to collect and store data seems to have surpassed our ability to make sense of it!. 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 . 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. Remco Chang. Assistant . Professor. Department of Computer Science. Tufts University. Big Data Visual Analytics:. A User-Centric Approach. “The . computer is incredibly fast, accurate, and stupid. Man is unbelievably slow, inaccurate, and brilliant. The marriage of the two is a force beyond calculation. 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 . 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:. . IIA Seminar . May 13, 2016. Audit & Advisory. The New Path to Value Creation. Creative destruction. Data trends. Future of Internal Audit. People, Process & Technology Challenges. Practical Frameworks . 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 . 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.

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