PPT-Exploratory data analysis (EDA)

Author : karlyn-bohler | Published Date : 2017-04-20

Detective Alex Yu cyuapuedu What isnt EDA EDA does not mean lack of planning or messy planning I dont know what I am doing just ask as many questions as possible

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Exploratory data analysis (EDA): Transcript

Detective Alex Yu cyuapuedu What isnt EDA EDA does not mean lack of planning or messy planning I dont know what I am doing just ask as many questions as possible in the survey I dont need a wellconceptualized research question or a wellplanned research design Just explore. Exploratory . Dialogue. Rebecca . Ferguson . @. R3beccaF. Simon Buckingham . Shum . @. sbskmi. Learning Analytics and Knowledge 2011, Banff. SocialLearn . Project. The . Open University, UK. www.open.ac.uk/sociallearn. Summer 2015. True or false?. A student’s college major determines her or his career path. False!. Got Evidence?. 2006 Study. 80,574 students - 87 colleges - 5 years. Good grades closely related to having a major close to one’s . Electrodermal. Activity. Akane. Sano and Rosalind W. Picard, . Massachusetts Institute of Technology . Media Lab. Affective Computing Group. akanes@mit.edu . What is . Electrodermal Activity?. Electrical measures of sweat gland . Classification of Sleep data. Akane. Sano. akanes@mit.edu. Affective Computing Group. Media Lab. Polysomnography. Multi-parametric test to evaluate . s. leep. Data and Labels. Data:. Healthy . students . Slide #. 1. Bivariate EDA. Describe the . relationship between pairs of . variables. Four characteristics to describe. Association (Direction). Form. Outliers. Strength. Quantitative Bivariate EDA. Slide #. Slide #. 1. Univariate EDA. Purpose – describe the distribution. Distribution . is concerned with what values a variable takes and how often it takes each value. Four characteristics. Shape. Outliers. for AKBC . Problems. Bhavana Dalvi . and William W. Cohen. School Of Computer Science. , . Carnegie Mellon University. . Motivation. Modeling . . . Unobserved . Classes. Multi-view . Exploratory EM. An Overview . (invited paper). Andrew B. Kahng. †‡. and Farinaz Koushanfar. †∗. . †. ECE and . ‡. CSE Depts., UC San Diego. *. ECE Dept., Rice University . {. abk,fkoushanfar. }@. ucsd.edu. Remark: covers Chapter 3 of the Tan book in Part. Organization. Why Exloratory Data Analysis?. Summary Statistics. Visualization. 1. Why Data Exploration?. Key motivations of data exploration include. Semi-supervised Learning . in the presence of unanticipated classes. Bhavana. . Dalvi. , William W. Cohen, Jamie . Callan. . School of Computer Science,. Carnegie Mellon University. Jim Trentadue. Software Quality Consulting Director. Original Software. jtrentadue@origsoft.com. Agenda. 2. What drives ET. 3. Good automation practice with ET . 4. 5. Case Study. Enabling Test Automation for ET . PDS Conformance Overview. 5. PDS Conformance. The . existence of PDS data is easily identified . on the EDA Contract Query Results screen in the PDS column. All . PDS data begins in PDS Evaluate . (“E”). . Michia. Burnett & . Latai. Wiggins. What Task Does The Robot Performs ?. The Exploratory Robot Are Used In Situations That Are Not Safe For A Situation . That Can Harm Humans . They . Can Only Do Basic Tasks Such As Jump , Roll , & Run .. 6. th. March 2018. Vung. Tau, Vietnam. Ashutosh Roychowdhury. Director. What is EDA?. . EDA stands for . E. lectronic . D. ocument . A. rchive.. . EDA is a web based Document Archiving. . and Revision Management Solution..

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