PPT-Sampling : Your data is only as good

as your field technicians Jeff Martin Astrea Taylor amp Nancy Zikmanis Next Phase of the Project Life Cycle PLANNING SAMPLING ASSESSMENT EVALUATION Plan for data collection using the DQO . Jared Hockly - Western Springs College . hocklyj@wsc.school.nz. . Overview of this session:. Discuss the standards 1.10 and 2.9 briefly (some clarifications). Focus on developing understanding of sampling variability. Big Question: How do you know when you have collected enough data and done it appropriately?. Today’s Agenda. Tips and Tricks. Article Review Discussion. Assignment for Feb 24. Sampling Review. Observation Approaches (Qualitative/ Quantitative) and Practice. . for. . Qualitative. . Research. Assoc. . Prof. Dr. Şehnaz . Şahinkarakaş. Sampling. S. ample. :. any . part of a population of individuals on whom information is obtained: students, teachers, young learners, etc. Michael Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, . Richard . Peng. , Aaron Sidford . M.I.T.. Outline. Reducing Row Count. Row . S. ampling and Leverage Scores. Adaptive Uniform Sampling. Yu Su*, Gagan Agrawal*, . Jonathan Woodring. #. Kary Myers. #. , Joanne Wendelberger. #. , James Ahrens. #. *The Ohio . State University. #. Los . Alamos National . Laboratory. Motivation. Science becomes increasingly data driven;. Zhizhuo. Zhang . Outline. Review of Mixture Model and EM algorithm. Importance Sampling. Re-sampling EM. Extending EM. Integrate Other Features. Result. Review Motif Finding: Mixture modeling. Given a dataset . Sketching. for streaming. Alexandr. . Andoni. (MSR). Application: Streaming. IP. Frequency. 131.107.65.14. 3. 18.0.1.12. 2. 80.97.56.20. 2. 131.107.65.14. 131.107.65.14. 131.107.65.14. 18.0.1.12. 18.0.1.12. Scott Braun Jason . Dunion. Peter . Colarco. TS or Hurricane SAL Pattern. TS or Hurricane SAL Pattern. Objectives: To measure the structure of the SAL (AEJ structure, warm and dry air, dust) in relationship to a tropical cyclone, with specific focus on the radial structure of the SAL/TC. From Surveys to Big . D. ata. Edith Cohen. Google Research. Tel Aviv University. Disclaimer:. Random sampling is classic and well studied tool with enormous impact across disciplines. This presentation is biased and limited by its length, my research interests, experience, understanding, and being a Computer Scientist. I will attempt to present some big ideas and selected applications. I hope to increase your appreciation of this incredible tool.. NABat. ). . Purpose. Create a continental-wide . program to monitor bats at local to range-wide . scales. Provide data to promote . effective conservation decision-making and . long-term . viability of bat populations across the continent. . Lemonade from Lemons. Bugs manifest themselves every where in deployed systems.. Each manifestation gives us the chance of inspection and hence the resolutions.. Deployment gives more test cases than the test suite.. Krishna . Pacifici. Department of Applied Ecology. NCSU. January 10, 2014. Designing studies. Why, what, and how?. Why collect the data?. What type of data to collect?. How should the data be collected in the field and then analyzed?. William P. Wattles, Ph.D.. I got the job!!! I am the new Human Resource Recruitment Specialist for . …I . would be involved in all branches. BEST PART... most of my job has to do with job analysis and performance, retention and turnover trends! (ALL STATISTICS and Behavior analysis) I will always apply what I learned at Francis Marion . Ke. Yi. Hong Kong University of Science and Technology. yike@ust.hk. Random Sampling on Big Data. 2. “Big Data” in one slide. The 3 V’s. : . Volume. External memory algorithms. Distributed data.