PPT-Random Sampling on Big Data: Techniques and Applications

Ke Yi Hong Kong University of Science and Technology yikeusthk Random Sampling on Big Data 2 Big Data in one slide The 3 Vs Volume External memory algorithms Distributed data. RAN#. Random Sampling using Ran#. The Ran#: Generates . a pseudo . random number to 3 decimal places that . is less than 1.. i.e. . it generates a random number in the range . [0, 1. ]. . Ran#. . is in Yellow. Basic Terms. Research units – subjects, participants. Population of . interest (all humans?). Accessible . population – those you can actually try to sample. Intended . sample – those you select for participation. Anup. Bhattacharya. IIT Delhi. . Joint work with Davis . Issac. (MPI), . Ragesh. . Jaiswal. (IITD) and Amit Kumar (IITD). Introduction: Sampling. Select a subset of data. Computations on “representative” subset would approximate computations on whole data. 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;. How do Sociologists choose the participants for their research?. Starter. Think. - Work independently for 2 minutes to write in as many key concepts into the worksheet as you can. . Pair. - Now, work in a pair with the person sitting next to you for 2 minutes and help each other with any key concepts you couldn't do on your own.. 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.. Designing experiments. OUTLINE of topics. Avoid obvious problems with:. The question. Sampling. Variables. How to do sampling.. Principles of experimental design.. Data collection. Consider the following 3 research questions:. 1. Sampling. A . sample. is a subset of the . population. In a . sample. , you study a few members of the population. In a . census. , you study every member of the population. If done properly, a sample can be accurate and avoid the cost and time needed for a full census . How . can it be that mathematics, being after all a product of human thought independent of experience, is so admirably adapted to the objects . of reality. Albert Einstein. Some parts of these slides were prepared based on . 7. Introduction. In . a typical statistical inference problem, you want to discover one or more characteristics of a given population. .. However, it is generally difficult or even impossible to contact each member of the population.. Keynote. BigDat. 2015: International Winter School on Big Data. Tarragona, Spain, January 26-30, 2015. January 26 2015. Geoffrey . Fox . gcf@indiana.edu. . . http://www.infomall.org. School of Informatics and Computing. Naturalistic Observation. Ecological vs. External vs. Internal Validity. Observational Studies. Case Study. N = 1. Patient KC: suffered brain damage in accident . Complete damage to episodic memory. Start Here--- https://bit.ly/41cD43F ---Get complete detail on 301B exam guide to crack F5 Certified Technology Specialist - Local Traffic Manager (F5-CTS LTM). You can collect all information on 301B tutorial, practice test, books, study material, exam questions, and syllabus. Firm your knowledge on F5 Certified Technology Specialist - Local Traffic Manager (F5-CTS LTM) and get ready to crack 301B certification. Explore all information on 301B exam with number of questions, passing percentage and time duration to complete test. If you\'re looking to embark on a journey to master Big Data through Hadoop, the Hadoop Big Data course at H2KInfosys is your ideal destination. Let\'s explore why this course is your gateway to Big Data success.

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