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1	The Application of Synthetic Aperture Radar (SAR) to Natural Resource Damage Assessment George Graettinger, GIS Project Manager Jay Coady, Nicolas Eckhardt, Mathew Dorsey, and Paul Whelan NOAA’s Ocean Service, Office o Add to Reading List Source URL: coastalgeotools.org Language: English - Date: 2015-04-23 14:28:17 Sar Technology Computing Statistics Radar Synthetic aperture radar National Oceanic and Atmospheric Administration
2	THE APPLICATION OF SYNTHETIC APERTURE RADAR (SAR) TO DWH NATURAL RESOURCE DAMAGE ASSESSMENT George Graettinger, Jay Coady, Nicolas Eckhardt, and Mathew Dorsey, NOAA Office of Response and Restoration NOAA’s Office of R Add to Reading List Source URL: coastalgeotools.org Language: English - Date: 2015-04-23 15:04:06 Environment Halliburton Oil spill Watercraft Deepwater Horizon Office of Response and Restoration Emergency management Deepwater Horizon oil spill BP Transocean
3	Data Mining Misconceptions #1: The[removed]Problem By Tim Graettinger This fall will mark my twentieth year as a data mining professional. Thank you. During that time, I worked at five different companies – mostly startu Add to Reading List Source URL: www.discoverycorpsinc.com Language: English - Date: 2009-08-14 16:47:01 Gain Predictive analytics Science Statistics Machine learning Statistical classification
4	So You Want a Job: Interviewing Data Miners and Modelers By Tim Graettinger Judging by the recent posts on numerous web sites, many companies are looking for people Add to Reading List Source URL: www.discoverycorpsinc.com Language: English - Date: 2011-07-20 00:01:08 Recruitment Janet Question Social psychology Management Behavior Employment Interviews Job interview
5	Data Mining Lessons from “Moneyball” By Tim Graettinger If you are a data miner, there’s a good chance you saw the movie, Moneyball, starring Brad Pitt and Jonah Hill, based Add to Reading List Source URL: www.discoverycorpsinc.com Language: English - Date: 2012-05-08 19:37:44 Bill James Economic model Moneyball Paul DePodesta Billy Beane Conceptual model Brad Pitt Mathematical model Data mining Baseball Oakland Athletics Baseball statistics
6	Data Mining Questions? Some Back-of-the-Envelope Answers By Tim Graettinger Data mining 1 , the discovery and modeling of hidden patterns in large volumes of data, is becoming a mainstream technology. And yet, for many, Add to Reading List Source URL: www.discoverycorpsinc.com Language: English - Date: 2009-08-14 16:52:06 Data management Statistics Predictive analytics Mining Database Spreadsheet Data Business intelligence Data analysis Data mining
7	Data Mining Misconceptions #2: How Much Data … By Tim Graettinger “How much data do I need for data mining?” In my experience, this is the most-frequently-asked of all frequently-asked questions about data mining. Add to Reading List Source URL: www.discoverycorpsinc.com Language: English - Date: 2010-11-10 21:25:19 Data analysis Model selection Data mining Overfitting Cross-validation Mathematical model Predictive analytics Statistics Machine learning Regression analysis
8	Grab Bag: Frequently-Asked Data Mining Questions and Answers By Tim Graettinger For the past year, I have presented a data mining “nuts and bolts” session during a Add to Reading List Source URL: www.discoverycorpsinc.com Language: English - Date: 2011-05-10 21:49:48 Information Data analysis Data mining Association rule learning Computer-aided audit tools Mining Database Data driven journalism Data management Science Information technology management
9	Nuts and Bolts of Data Mining: Classifiers & ROC Curves By Tim Graettinger Yes or no. Buy or sell. Renew or cancel. Many customer behaviors have this flavor of a choice between two alternatives. Suppose software called a Add to Reading List Source URL: www.discoverycorpsinc.com Language: English - Date: 2012-02-16 18:17:45 Data mining Detection theory Receiver operating characteristic Socioeconomics Classifier Gain Predictive analytics Statistics Statistical classification Machine learning
10	Nuts and Bolts of Data Mining: The Histogram By Tim Graettinger Practitioners, business users, developers, and academics love new data mining tools and methods. And yet, successful data mining requires much more than pow Add to Reading List Source URL: www.discoverycorpsinc.com Language: English - Date: 2012-06-18 18:52:40 Data analysis Histogram Mode Frequency distribution Outlier Plot RANSAC Anomaly detection Statistics Summary statistics Robust statistics

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