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DTSTART;TZID=America/Los_Angeles:20151015T090000
DTEND;TZID=America/Los_Angeles:20151015T170000
DTSTAMP:20260516T023443
CREATED:20160504T145420Z
LAST-MODIFIED:20160504T145420Z
UID:1740-1444899600-1444928400@datascience.uci.edu
SUMMARY:Introduction to Next Generation Sequencing Data Analysis
DESCRIPTION:The workshop teaches the basics of using command line software to analyze NGS data such as RNA-seq and ChIP-seq. \n \nDescription: \n \nThe workshop teaches the basics of using command line software to analyze NGS data such as RNA-seq and ChIP-seq. \nWe cover NGS workflow\, general data analysis pipeline\, short read alignment software\, general workflow for DNA-seq\, RNA-seq and ChIP-seq and corresponding software resources. We also cover popular pipeline for RNA-seq as well as R based statistical analysis of gene expression. \nPrerequisite: Introduction to Linux\nDate:October 15th\, 2015\nTime: 9 a.m. to 5 p.m. with lunch provided\nLocation: Donald Bren Hall\, Room 3011\nInstructor:Jenny Wu\, UC Irvine
URL:https://datascience.uci.edu/event/introduction-to-next-generation-sequencing-data-analysis/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20151012T090000
DTEND;TZID=America/Los_Angeles:20151012T170000
DTSTAMP:20260516T023443
CREATED:20160504T145314Z
LAST-MODIFIED:20160504T145314Z
UID:1739-1444640400-1444669200@datascience.uci.edu
SUMMARY:Introduction to Linux
DESCRIPTION:This course is for researchers who have never used Linux and/or a compute cluster and introduces concepts and best practices for both. \n \nDescription: \n \nThis course is for researchers who have never used Linux and/or a compute cluster and introduces concepts and best practices for both. \nThis course covers how to best exploit the bash shell for both interactive work and batch jobs\, moving & simple manipulation of data\, as well very short introductions to programming in bash\, Perl\, and R. This is not computer science; this is a driver’s license. \nThis workshop is the same as the one offered on the 5th \nDate:October 12th\, 2015\nTime: 9 a.m. to 5 p.m. with lunch provided\nLocation: Donald Bren Hall\, Room 3011\nInstructor: Harry Mangalam\, OIT/HPC\, UC Irvine\nPre-requisites: For the course\, none. For the tutorial\, a laptop with WiFi\, with a terminal application (Macs have the Terminal app; Windows need putty)\, and both would benefit from ‘x2go’.
URL:https://datascience.uci.edu/event/introduction-to-linux-2/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20151009T090000
DTEND;TZID=America/Los_Angeles:20151009T170000
DTSTAMP:20260516T023443
CREATED:20160504T145155Z
LAST-MODIFIED:20160504T145155Z
UID:1738-1444381200-1444410000@datascience.uci.edu
SUMMARY:Predictive Modelling with Python
DESCRIPTION:Learn about the practice of predictive modeling using Python. \n \nDescription: \n \nLearn about the practice of predictive modeling using Python. \nTo find out about future one-day courses\, please join our mailing list. \nPython is a popular language for scientific processing and machine learning. This course will introduce general modeling concepts in addition to concrete examples based on the scikit-learn library. Example usage of scikit-learn will illustrate how to fit and evaluate predictive models. Regression and classification settings will be considered. The course will be taught mostly through the medium of iPython notebooks. \nThis course is targeted primarily at graduate students who have not already taken a full course in machine learning. \nDate:October 9 \, 2015 \nTime: 9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 3011 \nInstructors: Kevin Bache\, Eric Nalisnick\, Brian Vegetabile \nPrerequisites: basic familiarity with Python (prior experience with scikit-learn is not necessary). To understand the more theoretical aspects of the course\, it is recommended to have knowledge of linear algebra\, probability\, and calculus. \nGitHub repository
URL:https://datascience.uci.edu/event/predictive-modelling-with-python/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20151005T090000
DTEND;TZID=America/Los_Angeles:20151005T170000
DTSTAMP:20260516T023443
CREATED:20160504T144855Z
LAST-MODIFIED:20160504T144855Z
UID:1737-1444035600-1444064400@datascience.uci.edu
SUMMARY:Introduction to Linux
DESCRIPTION:This course is for researchers who have never used Linux and/or a compute cluster and introduces concepts and best practices for both. \n \nDescription: \n \nThis course is for researchers who have never used Linux and/or a compute cluster and introduces concepts and best practices for both. \nThis course covers how to best exploit the bash shell for both interactive work and batch jobs\, moving & simple manipulation of data\, as well very short introductions to programming in bash\, Perl\, and R. This is not computer science; this is a driver’s license. \nDate:October 5th\, 2015\nTime: 9 a.m. to 5 p.m. with lunch provided\nLocation: Donald Bren Hall\, Room 3011\nInstructor: Harry Mangalam\, OIT/HPC\, UC Irvine\nPre-requisites: For the course\, none. For the tutorial\, a laptop with WiFi\, with a terminal application (Macs have the Terminal app; Windows need putty)\, and both would benefit from ‘x2go’.
URL:https://datascience.uci.edu/event/introduction-to-linux/
ATTACH;FMTTYPE=image/png:https://datascience.uci.edu/wp-content/uploads/2014/09/lock_data_science.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150925T100000
DTEND;TZID=America/Los_Angeles:20150925T170000
DTSTAMP:20260516T023443
CREATED:20160504T143835Z
LAST-MODIFIED:20160504T143835Z
UID:1735-1443175200-1443200400@datascience.uci.edu
SUMMARY:Third Annual Microbiome Connections
DESCRIPTION:RSVP for the Third Annual Microbiome Connections on Friday\, Sept. 25\, 2015 \n \nDescription: \nRSVP for the Third Annual Microbiome Connections on Friday\, Sept. 25\, 2015. \n  \nDate: Friday September 25\, 2015\nTime: 10:00 a.m. – 5:00 p.m \nLocation: Calit2\, UC Irvine – Directions \n\n  \n\nAGENDA \nEnvironment Session \n\n10:00 am \nKatherine Mackey\, UC Irvine\n\nExploring Evolutionary Drivers of Phytoplankton Diversity and Distribution \n\n10:40 am \n Allon Hochbaum\, UC Irvine\n\nEngineering Microbial Communities through Interspecies Interaction \n\n11:20 am \nJack Gilbert\, Argonne National Laboratory\n\nInvisible Influence: Microbiomes of Our Built Environment \n–Lunch Provided– \nHealth Session \n\n1:00 pm \nAmir Zarrinpar\, UC San Diego\n\nCyclical Fluctuations of the Gut Microbiome and Its Relationship to Host Metabolism\n\n1:40 pm Paolo Sassone-Corsi\, UC Irvine\n\nGut Microbiome and the Circadian Clock\n \n\n2:20 pm \nRob Knight\, UC San Diego\n\nDynamics of the Gut Microbiome\n–Coffee Break–\n \n\n3:20 pm \nElizabeth Costello\, Stanford\n\nHuman Microbiome Dynamics in the First 1\,000 Days of Life\n\n4:00 pm Sarkis Mazmanian\, Caltech\n\nThe Gut-Microbiome-Brain Connection\n \n\n4:40 pm \nDiscussion\n\n \n–Wine and Cheese Reception– \nThis no-cost symposium is co-hosted by UCI’s Institute for Genomics and Bioinformatics\, Calit2 and the Data Science Initiative at UC Irvine. \nKindly RSVP
URL:https://datascience.uci.edu/event/third-annual-microbiome-connections/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150917T040000
DTEND;TZID=America/Los_Angeles:20150917T183000
DTSTAMP:20260516T023443
CREATED:20160504T143129Z
LAST-MODIFIED:20160504T143129Z
UID:1734-1442462400-1442514600@datascience.uci.edu
SUMMARY:Data Science End-of-Summer Event
DESCRIPTION:Please join us on the afternoon of Thursday\, September 17th to learn more about the UCI Data Science Initiative fellows’ summer research projects. \n \nDescription: \n\nPlease join us on the afternoon of Thursday\, September 17th to learn more about the UCI Data Science Initiative fellows’ summer research projects. \nDate: Thursday\, September 17\nTime: 4-6:30pm\n\nLocation: Calit2 Auditorium\nSeveral DSI summer fellows will present on their research from 4-5pm. Afterwards the fellows will present posters in the Calit2 lobby. Refreshments will be served during the poster session. \nThis event is free and open to the public. An RSVP is required. \nFor future announcements please subscribe to our email list:https://maillists.uci.edu/mailman/listinfo/datascience-initiative
URL:https://datascience.uci.edu/event/data-science-end-of-summer-event/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150701T160000
DTEND;TZID=America/Los_Angeles:20150701T170000
DTSTAMP:20260516T023443
CREATED:20160504T141357Z
LAST-MODIFIED:20160504T141357Z
UID:1733-1435766400-1435770000@datascience.uci.edu
SUMMARY:New Tools and Trends in Reproducible Research
DESCRIPTION:The UCI Data Science Initiative is pleased to announce a special lecture on open science by Kyle Cranmer\, experimental particle physicist and professor at New York University.\n \n \nDescription: \n\n\nThe UCI Data Science Initiative is pleased to announce a special lecture on open science by Kyle Cranmer\, experimental particle physicist and professor at New York University.\n \n\nDate: Wednesday\, July 1\nTime: 4 to 5 p.m.\n\nLocation: Calit2 Auditorium\n \n\n \nAbstract:\nIn the last few years a new digital infrastructure has emerged around reproducible research and open science. I will review some of these tools and services and describe their connection to his scientific research. I will comment about strategies that can be used to bring about change in a field’s norms and practices.\n \nAbout the Speaker:\nKyle Cranmer is an experimental particle physicist and professor at New York University at the Center for Cosmology and Particle Physics\, and Affiliated Faculty member at NYU’s Center for Data Science.\n>> View website
URL:https://datascience.uci.edu/event/new-tools-and-trends-in-reproducible-research/
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2015/06/cranmer.kyle_.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150624T130000
DTEND;TZID=America/Los_Angeles:20150624T160000
DTSTAMP:20260516T023443
CREATED:20160504T140944Z
LAST-MODIFIED:20160504T140944Z
UID:1732-1435150800-1435161600@datascience.uci.edu
SUMMARY:Stan for Bayesian Inference
DESCRIPTION:Stan is an open-source\, Bayesian inference tool with interfaces in R\, Python\, Matlab\, Julia\, Stata\, and the command line. Users write statistical models in a high-level statistical language. \n \nDescription: \nStan is an open-source\, Bayesian inference tool with interfaces in R\, Python\, Matlab\, Julia\, Stata\, and the command line. Users write statistical models in a high-level statistical language. \nTo find out about courses\, please join our mailing list. \nStan is an open-source\, Bayesian inference tool with interfaces in R\, Python\, Matlab\, Julia\, Stata\, and the command line. Users write statistical models in a high-level statistical language. The default Bayesian inference algorithm is the no-U-turn sampler (NUTS)\, an auto-tuned version of Hamiltonian Monte Carlo. Stan was developed to address the speed and scalability issues of existing Bayesian inference tools. \nThe goal of the workshop is the practical application of Stan to different models. \nDate: Wednesday\, June 24\, 2015 \nTime: 1 p.m. to 4 p.m. with coffee provided \nLocation: Donald Bren Hall\, Room 3011 \nInstructor: Daniel Lee \nPre-requisites: Experience in Bayesian statistical modeling is recommended\, but not required. \n 
URL:https://datascience.uci.edu/event/stan-for-bayesian-inference/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150601T120000
DTEND;TZID=America/Los_Angeles:20150601T130000
DTSTAMP:20260516T023443
CREATED:20160504T140752Z
LAST-MODIFIED:20160504T140752Z
UID:1731-1433160000-1433163600@datascience.uci.edu
SUMMARY:Global Burden of Disease Study
DESCRIPTION:A Systematic Analysis for the Global Burden of Disease Study 2013\n \nDescription: \n\nA Systematic Analysis for the Global Burden of Disease Study 2013\nMonday\, June 1\, 2015\n12:00 PM – 1:00 PM\nCalit2 Auditorium \nSpeaker: Bryan L. Sykes\, Ph.D.\, Assistant Professor\, Department of Criminology\, Law and Society & Program in Public Health\, University of California\, Irvine \nAbstract:\nThe Global Burden of Diseases\, Injuries\, and Risk Factors 2013 Study (GBD 2013) is the first of a series of annual revisions for the GBD studies that began with estimates for 1990 and were most recently updated to the year 2010. Results for specific diseases and impairments have been extensively published\, drawing attention to the importance of disability from musculoskeletal disorders\, mental and substance abuse disorders\, and a variety of other non-communicable diseases. Given the ambitious goal of the GBD 2010\, to synthesize the global evidence on the country-age-sex-year prevalence of all major conditions\, a number of specific estimates have been critiqued. Specific data sources\, modeling assumptions\, and aspects of the general approach have been challenged and there is widespread recognition that more and higher quality data could improve the estimates. The GBD 2013 provides an opportunity to incorporate constructive critical commentary on GBD 2010 data sources\, model development\, methods\, and interpretation. Additionally\, the GBD 2013 reflects methodological advances and includes new data on disability weights\, capturing many newly published or unpublished data sources for the conditions included in the GBD. In this talk I summarize key findings and methodological changes in our forthcoming paper\, which analyzes over 30\,800 epidemiological sources from 188 countries spanning the last three decades to provide the most up-to-date empirical assessment of the leading causes of acute disease incidence\, chronic disease prevalence\, and YLDs since 1990. Globally\, we find that in 2013 only 4.3% of the population had no GBD disease or injury sequelae\, up slightly from 4.2% in 1990.Biography:\nBryan Sykes is an Assistant Professor of Criminology\, Law and Society (and\, by courtesy\, Sociology and Public Health). His research focuses on demography and criminology\, with particular interests in fertility\, health\, mass imprisonment\, and social inequality. He has been a National Science Foundation Minority Post-Doctoral Research Fellow at the University of Washington\, a Visiting Scholar in the Institute for Research on Poverty (IRP) at UW-Madison\, and a Research Associate at the National Economics Research Associates\, the National Board of Medical Examiners\, and Nickerson & Associates LLC. Professor Sykes has taught courses on demography\, research methods\, power and social social stratification\, criminology\, and public policy\, and he has received awards from the Department of Demography at the University of California-Berkeley\, the National Board of Medical Examiners\, and the Population Association of America. His research has appeared in several volumes of The Lancet\, The Annals of the American Academy of Political and Social Science\, and is forthcoming in issues of Sociological Forum and The Russell Sage Foundation Journal of the Social Sciences. Findings from his research have been covered by The American Association for the Advancement of Science (AAAS)\, The New York Times\, The Guardian\, Time Magazine\, NPR\, and other news outlets.\nFor updates\, please check our website: publichealth.uci.edu or contact\nAnna Rager\, 949-824-0566 or Arager@uci.edu
URL:https://datascience.uci.edu/event/global-burden-of-disease-study/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150529T150000
DTEND;TZID=America/Los_Angeles:20150529T180000
DTSTAMP:20260516T023443
CREATED:20160504T104932Z
LAST-MODIFIED:20160504T104932Z
UID:1730-1432911600-1432922400@datascience.uci.edu
SUMMARY:An Afternoon of Data Science: A Data Science Initiative Year-End Event
DESCRIPTION:The UCI Data Science Initiative invites you to attend a year-end afternoon event on Friday\, May 29 to network with colleagues\, enjoy a poster session highlighting the latest research in data science\, and hear updates and news about the Initiative. \n \nDescription: \n \nThe UCI Data Science Initiative invites you to attend a year-end afternoon event on Friday\, May 29 to network with colleagues\, enjoy a poster session highlighting the latest research in data science\, and hear updates and news about the Initiative. \nDate: Friday\, May 29\, 2015\nTime: 3:00 to 6:00 p.m.\nLocation: Calit2 Auditorium\, Lobby\, and Patio\, UCI (directions) \n\n \nAgenda: \n\n\n\n\n3:00 p.m.\nHighlights from a Year of Data Science at UCI\nProfessor Padhraic Smyth\, UCI Data Science Initiative Director\n\n\n3:30 p.m.\nDeepDive: A Data System for Macroscopic Science\nProfessor Chris Re\, Department of Computer Science\, Stanford University (abstract below)\n\n\n4:30 p.m. – 6:00 p.m.\nReception\, Social Networking\, and Graduate Student Poster Session\n\n\n\n\n  \nGraduate Student Posters Session\nDetails are available on the Graduate Student Posters Webpage. \nDeepDive: A Data System for Macroscopic Science\nProfessor Chris Re\nDepartment of Computer Science\, Stanford University \n>>View Chris Re’s Webpage\n>>Find out about DeepDive \nMany pressing questions in science are macroscopic\, as they require scientists to integrate information from numerous data sources\, often expressed in natural languages or in graphics; these forms of media are fraught with imprecision and ambiguity and so are difficult for machines to understand. Here I describe DeepDive\, which is a new type of system designed to cope with these problems. It combines extraction\, integration and prediction into one system. For some paleobiology and materials science tasks\, DeepDive-based systems have surpassed human volunteers in data quantity and quality (recall and precision). DeepDive is also used by scientists in areas including genomics and drug repurposing\, by a number of companies involved in various forms of search\, and by law enforcement in the fight against human trafficking. DeepDive does not allow users to write algorithms; instead\, it asks them to write only features. A key technical challenge is scaling up the resulting inference and learning engine\, and I will describe our line of work in computing without using traditional synchronization methods including Hogwild! and DimmWitted. \nDeepDive is open source on github and available from DeepDive.stanford.edu. \nChris Re Bio\nChristopher (Chris) Re is an assistant professor in the Department of Computer Science at Stanford University and a Robert N. Noyce Family Faculty Scholar. His work’s goal is to enable users and developers to build applications that more deeply understand and exploit data. Chris received his PhD from the University of Washington in Seattle under the supervision of Dan Suciu. For his PhD work in probabilistic data management\, Chris received the SIGMOD 2010 Jim Gray Dissertation Award. He then spent four wonderful years on the faculty of the University of Wisconsin\, Madison\, before moving to Stanford in 2013. He helped discover the first join algorithm with worst-case optimal running time\, which won the best paper at PODS 2012. He also helped develop a framework for feature engineering that won the best paper at SIGMOD 2014. In addition\, work from his group has been incorporated into scientific efforts including the IceCube neutrino detector and PaleoDeepDive\, and into Cloudera’s Impala and products from Oracle\, Pivotal\, and Microsoft’s Adam. He received an NSF CAREER Award in 2011\, an Alfred P. Sloan Fellowship in 2013\, and a Moore Data Driven Investigator Award in 2014. Chris was an early member of and continues to be an adviser to Context Relevant.
URL:https://datascience.uci.edu/event/an-afternoon-of-data-science-a-data-science-initiative-year-end-event/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150529T110000
DTEND;TZID=America/Los_Angeles:20150529T120000
DTSTAMP:20260516T023443
CREATED:20160504T104759Z
LAST-MODIFIED:20160504T104759Z
UID:1729-1432897200-1432900800@datascience.uci.edu
SUMMARY:Big Data\, Data Science
DESCRIPTION:Big Data\, Data Science\, and other Buzzwords that Really Matter\nMichael Franklin\, Thomas M. Siebel Professor and Chair\, UC Berkeley\nComputer Science Distinguished Lecture Series \n \nDescription: \n \nBig Data\, Data Science\, and other Buzzwords that Really Matter\nMichael Franklin\, Thomas M. Siebel Professor and Chair\, UC Berkeley\nComputer Science Distinguished Lecture Series \nDate: May 29\, 2015\nTime: 11:00 AM – 12:00 PM\nLocation: Donald Bren Hall 6011 \n>>View event flyer \nAbstract:\nData is all the rage across industry and across campuses. While it may be temping to dismiss the buzz as just another spin of the hype cycle\, there are substantial shifts and realignments underway that are fundamentally changing how Computer Science\, Statistics and virtually all subject areas will be taught\, researched\, and perceived as disciplines. In this talk I will give my personal perspectives on this new landscape based on experiences organizing a large\, industry-engaged academic Computer Science research project (the AMPLab)\, in helping to establish a campus-wide Data Science research initiative (the Berkeley Institute for Data Science)\, and my participation on a campus task force charged with mapping out Data Science Education for all undergraduates at Berkeley. \nBio:\nMichael Franklin is the Thomas M. Siebel Professor of Computer Science and Chair of the Computer Science Division of the EECS Department at UC Berkeley. He is director of the Berkeley AMPLab\, a 70+ person effort fusing scalable computing\, machine learning\, and human computation to make sense of data at scale. AMPLab software including: Spark\, Shark\, and Mesos\, plays a significant role in the emerging Big Data ecosystem. The lab is funded by an NSF CISE Expeditions Award\, the Darpa XData program\, and 26 companies including founding sponsors Amazon Web Services\, Google\, and SAP. \nContact: Melanie Sanders
URL:https://datascience.uci.edu/event/big-data-data-science/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150520T080000
DTEND;TZID=America/Los_Angeles:20150520T170000
DTSTAMP:20260516T023443
CREATED:20160504T104633Z
LAST-MODIFIED:20160504T104633Z
UID:1728-1432108800-1432141200@datascience.uci.edu
SUMMARY:Using Mobile Technology
DESCRIPTION:Using Mobile Technology to Understand Activity Space Exposures and Youth Development: Preliminary Findings from the Adolescent Health and Development in Context (AHDC) Study \nPresented by Christopher Browning\, Professor of Sociology at Ohio State University \n \nDescription: \nUsing Mobile Technology to Understand Activity Space Exposures and Youth Development: Preliminary Findings from the Adolescent Health and Development in Context (AHDC) Study \nPresented by Christopher Browning\, Professor of Sociology at Ohio State University \nDate: Wednesday\, May 20\nTime: 12:00 p.m.\nLocation: 112 Social Ecology 1 \n(lunch provided) \n>> View Event Flyer \nResearch examining contextual influences on youth development and behavior has entered a new era of possibility. The availability of mobile technology for GPS tracking and real-time assessment has opened the door to collection of far more precise data on everyday sociospatial contexts. This presentation describes the Adolescent Health and Development in Context (AHDC) project – a large scale\, longitudinal study of youth ages 11-17 in Franklin County\, Ohio focused on the nature and developmental consequences of routine activity space exposures. In addition to the major research questions and theoretical approach motivating the study\, I review key features of the study design: (1) Smartphone-based GPS tracking over the course of a week; (2) Smartphone-based Ecological Momentary Assessment (EMA) of location\, activities\, network partner presence\, risk behaviors\, and immediate social environments several times a day during the week; (3) follow-up interview-based collection of detailed space-time budget data on five of the seven days.  Preliminary results indicate that youth spend a substantial proportion of non-home time outside of their conventionally defined neighborhoods.  Moreover\, exposure to potentially criminogenic activity spaces varies significantly between youth from the same neighborhood and across days of the week (within youth).  I conclude with a discussion of the implications of activity space approaches and new data collection technologies for research on communities and crime.  \nChristopher R. Browning is a Professor of Sociology at Ohio State University.  His research interests include the causes and consequences of community social organization; the neighborhood context of crime\, risk behavior\, and health; the long-term effects of maltreatment during childhood; and multilevel statistical models.  \nPlease kindly RSVP by May 18th\, by following this link:  http://cls.soceco.uci.edu/ChrisBrowningTalk
URL:https://datascience.uci.edu/event/using-mobile-technology/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150508T090000
DTEND;TZID=America/Los_Angeles:20150508T170000
DTSTAMP:20260516T023443
CREATED:20160504T104505Z
LAST-MODIFIED:20160504T104505Z
UID:1727-1431075600-1431104400@datascience.uci.edu
SUMMARY:Text and Data Mining for Interactive Online Learning Minisymposium
DESCRIPTION:On May 8\, scholars from around the country will converge at Calit2 on the campus of UC Irvine to hold a mini symposium on text and data mining for interactive online learning. \n \nDescription: \n \nOn May 8\, scholars from around the country will converge at Calit2 on the campus of UC Irvine to hold a mini symposium on text and data mining for interactive online learning. \nDistinguished guest speakers will discuss computational discourse\, cognitive and predictive modeling\, crowdsourcing\, and peer assessment in digital learning environments.\n  \n \n\n \nVisit the Text and Data Mining for Interactive Online Learning event page for a list of speakers and more information.\n \n \n 
URL:https://datascience.uci.edu/event/text-and-data-mining-for-interactive-online-learning-minisymposium/
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150424T090000
DTEND;TZID=America/Los_Angeles:20150424T170000
DTSTAMP:20260516T023443
CREATED:20160504T104134Z
LAST-MODIFIED:20160504T104134Z
UID:1725-1429866000-1429894800@datascience.uci.edu
SUMMARY:Predictive Modeling with Python
DESCRIPTION:Learn about the practice of predictive modeling using Python. \n \nDescription: \n \nLearn about the practice of predictive modeling using Python. \nTo find out about future one-day courses\, please join our mailing list. \nPython is a popular language for scientific processing and machine learning. This course will introduce general modeling concepts in addition to concrete examples based on the scikit-learn library. Example usage of scikit-learn will illustrate how to fit and evaluate predictive models. Regression and classification settings will be considered. The course will be taught mostly through the medium of iPython notebooks. \nThis course is targeted primarily at graduate students who have not already taken a full course in machine learning. \nDate:October 9 \, 2015 \nTime: 9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructors: Kevin Bache\, Eric Nalisnick\, Brian Vegetabile \nPrerequisites: basic familiarity with Python (prior experience with scikit-learn is not necessary). To understand the more theoretical aspects of the course\, it is recommended to have knowledge of linear algebra\, probability\, and calculus. \n 
URL:https://datascience.uci.edu/event/predictive-modeling-with-python/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150413T090000
DTEND;TZID=America/Los_Angeles:20150413T170000
DTSTAMP:20260516T023443
CREATED:20160504T103641Z
LAST-MODIFIED:20160504T103641Z
UID:1724-1428915600-1428944400@datascience.uci.edu
SUMMARY:Introduction to Linux Short Course
DESCRIPTION:This course is for researchers who have never used Linux and/or a compute cluster and introduces concepts and best practices for both. Note: this course is a repeated offering of the earlier linux course on April 6. \n \nDescription: \n \nThis course is for researchers who have never used Linux and/or a compute cluster and introduces concepts and best practices for both. Note: this course is a repeated offering of the earlier linux course on April 6. \nThis course covers how to best exploit the bash shell for both interactive work and batch jobs\, moving & simple manipulation of data\, as well very short introductions to programming in bash\, Perl\, and R. This is not computer science; this is a driver’s license. \nDate: April 13th\, 2014\nTime: 9 a.m. to 5 p.m. with lunch provided\nLocation: Donald Bren Hall\, Room 3011\nInstructor: Harry Mangalam\, OIT/HPC\, UC Irvine\nPre-requisites: For the course\, none. For the tutorial\, a laptop with WiFi\, with a terminal application (Macs have the Terminal app; Windows need putty)\, and both would benefit from ‘x2go’.
URL:https://datascience.uci.edu/event/introduction-to-linux-short-course-3/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150410T090000
DTEND;TZID=America/Los_Angeles:20150410T170000
DTSTAMP:20260516T023443
CREATED:20160504T103511Z
LAST-MODIFIED:20160504T103511Z
UID:1723-1428656400-1428685200@datascience.uci.edu
SUMMARY:Introduction to R
DESCRIPTION:This course provides an introduction to the fundamentals of the R language. \n \nDescription: \n \nThis course provides an introduction to the fundamentals of the R language.\nIn this course\, you will learn how to program in R and how to effectively use R to do data analysis. The course covers introduction to data/object types in R\, reading data into R\, creating data graphics\, accessing and installing R packages\, writing R functions\, fitting statistical models including regression models and performing statical tests as t-test\, and ANOVA. Practical examples will be provided during the course. \nDate: Friday\, April 10\, 2015 \nTime: 9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Sepehr Akhavan\, Homer Strong\, Zhe Yu\, Yuxiao Wang\, Department of Statistics\, UC Irvine \nPre-requisites: 1) familiarity with basic statistical concepts\, and 2) basic programming knowledge. For the tutorial\, bring a laptop with R downloaded and installed and WiFi.
URL:https://datascience.uci.edu/event/introduction-to-r-3/
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2014/09/r-project-logo.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150406T090000
DTEND;TZID=America/Los_Angeles:20150406T170000
DTSTAMP:20260516T023443
CREATED:20160504T103326Z
LAST-MODIFIED:20160504T103326Z
UID:1722-1428310800-1428339600@datascience.uci.edu
SUMMARY:Introduction to Linux Short Course
DESCRIPTION:This course covers how to best exploit the bash shell for both interactive work and batch jobs\, moving & simple manipulation of data\, as well very short introductions to programming in bash\, Perl\, and R. This is not computer science; this is a driver’s license. \n \nDescription: \n \nThis course is for researchers who have never used Linux and/or a compute cluster and introduces concepts and best practices for both. \nThis course covers how to best exploit the bash shell for both interactive work and batch jobs\, moving & simple manipulation of data\, as well very short introductions to programming in bash\, Perl\, and R. This is not computer science; this is a driver’s license. \nDate: April 6th\, 2015\nTime: 9 a.m. to 5 p.m. with lunch provided\nLocation: Donald Bren Hall\, Room 3011\nInstructor: Harry Mangalam\, OIT/HPC\, UC Irvine\nPre-requisites: For the course\, none. For the tutorial\, a laptop with WiFi\, with a terminal application (Macs have the Terminal app; Windows need putty)\, and both would benefit from ‘x2go’.
URL:https://datascience.uci.edu/event/introduction-to-linux-short-course-2/
ATTACH;FMTTYPE=image/png:https://datascience.uci.edu/wp-content/uploads/2014/09/lock_data_science1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150313T100000
DTEND;TZID=America/Los_Angeles:20150313T170000
DTSTAMP:20260516T023443
CREATED:20160504T103141Z
LAST-MODIFIED:20160504T103141Z
UID:1721-1426240800-1426266000@datascience.uci.edu
SUMMARY:Networks: Algorithms\, Statistics\, and Social Science
DESCRIPTION:The UCI Data Science Initiative is hosting a one day symposium on recent research advances in network modeling and analysis. The interdisciplinary event will feature speakers from different backgrounds and perspectives\, including social network analysis\, criminology\, computer science\, and statistics. \n \nDescription: \n \nThe UCI Data Science Initiative is hosting a one day symposium on recent research advances in network modeling and analysis. The interdisciplinary event will feature speakers from different backgrounds and perspectives\, including social network analysis\, criminology\, computer science\, and statistics.\n \n \n \n\nTo attend this symposium\, please RSVP below. Attendance is free and open to all.\n \nDate: March 13\, 2015\nTime: 10:00 a.m. to  5:00 p.m.\nReception: 5:00 p.m. to 6:00 p.m. in the Calit2 lobby\nLocation: Calit2 Auditorium\, UCI (directions)\n \n \n\n\n\n\nTime00\nSpeakers\n\n\n\n\n 10:00\nIntroduction\nPadhraic Smyth\, UCI Data Science Initiative and Department of Computer Science\n\n\n\n 10:10\nStatistical Models for Social Networks – Some Points of Progress\nCarter Butts\, UC Irvine Department of Social Sciences\n\n\n 10:25\nModel-based Clustering of Large Networks\nDavid Hunter\, Penn State Department of Statistics\n\n\n 10:45\nExponential-family Approaches to Jointly Model Network Relations and Endogenous Attributes\nIan Fellows\, UCLA Department of Statistics\n\n\n11:15\nExponential Random Graph Models are Hard \nWill Devanny\, UC Irvine Department of Computer Science\n\n\n11:35\nComparing Local Structure in Social Networks\nKatherine Faust\, UC Irvine Department of Social Sciences\n\n\n 12:00\nBreak\n\n\n 1:30\nThe Information Life of Social Networks\nLada Adamic\, Facebook\n\n\n 2:15\nExtracting Local Structures from Geometric Networks\nDavid Mount\, University of Maryland Department of Computer Science\n\n\n 2:45\nSuper-scalable Inference for Cross-Sectional ERGMs and TERGMs with Multiple Observations\, via Edge Variable Thinning\nCarter Butts\, UC Irvine Department of Social Sciences\n\n\n 3:15\n Break\n\n\n 3:30\nNoise and Its Impact on Social Network Algorithms\nDavid Kempe\, University of Southern California Department of Computer Science\n\n\n 4:15\nPitfalls and Challenges in Stochastic Actor-Based Network/ Behavior Models\nJohn Hipp\, UC Irvine Department of Criminology\, Law and Society\n\n\n 4:45\nConstruction Algorithms for Online Social Networks\nAthina Markopoulou\, UC Irvine Department of Electrical Engineering and Computer Science\n\n\n 5:15\nReception in Calit2 Lobby\n\n\n\n\n\n\n  \nEvent Chairs \nCarter Butts (Social Sciences\, UCI)\, Michael Goodrich (Computer Science\, UCI)\, and Padhraic Smyth (Data Science Initiative\, UCI) \n\nThis event is co-sponsored by the UCI Data Science Initiative and the Office of Naval Research (ONR) Multidisciplinary University Research Initiative (MURI) program.
URL:https://datascience.uci.edu/event/networks-algorithms-statistics-and-social-science/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150309T120000
DTEND;TZID=America/Los_Angeles:20150309T130000
DTSTAMP:20260516T023443
CREATED:20160504T102706Z
LAST-MODIFIED:20160504T102706Z
UID:1719-1425902400-1425906000@datascience.uci.edu
SUMMARY:Project Tycho: using historical disease surveillance data to advance public health now
DESCRIPTION:A presentation by Professor Wilbert van Panhuis\, Department of Epidemiology at the University of Pittsburgh. \n \nDescription: \n \nA presentation by Professor Wilbert van Panhuis\, Department of Epidemiology at the University of Pittsburgh. \nDate: March 9\, 2015\nTime: 12:00 p.m. to 1:00 p.m.\nLocation: Calit2 Auditorium (directions) \nThis event is hosted by the UCI Program in Public Health and the Data Science Initiative. This event is part of a series of seminars that address contemporary issues in public health. \nFor more information\, please visit the UCI Public Health website.
URL:https://datascience.uci.edu/event/project-tycho-using-historical-disease-surveillance-data-to-advance-public-health-now/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150221T080000
DTEND;TZID=America/Los_Angeles:20150222T170000
DTSTAMP:20260516T023443
CREATED:20160502T115647Z
LAST-MODIFIED:20160502T115647Z
UID:1711-1424505600-1424624400@datascience.uci.edu
SUMMARY:Software Carpentry Workshop
DESCRIPTION:This hands-on workshop will cover basic concepts and tools\, including program design\, programming in Python\, version control and task automation in the Unix shell. Participants will be encouraged to help one another and to apply what they have learned to their own research problems. Please understand that space is limited! \n \nDescription: \n \n The UC Irvine Data Science Initiative is holding a two day workshop on the topic of Lab Skills for Scientific Computing by Software Carpentry Foundation on February 21-22 at Donald Bren School of Information and Computer Sciences. Software Carpentry’s mission is to help graduate students get more research done in less time and with less pain by teaching them basic lab skills for scientific computing. \nThis hands-on workshop will cover basic concepts and tools\, including program design\, programming in Python\, version control and task automation in the Unix shell. Participants will be encouraged to help one another and to apply what they have learned to their own research problems. Please understand that space is limited! \nDate: February 21-22\, 2015 \nLocation: Donald Bren Hall\, Room 4011 \nPre-requisites: Basic programming knowledge.
URL:https://datascience.uci.edu/event/software-carpentry-workshop/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150220T150000
DTEND;TZID=America/Los_Angeles:20150220T160000
DTSTAMP:20260516T023443
CREATED:20160502T110039Z
LAST-MODIFIED:20160502T110039Z
UID:1710-1424444400-1424448000@datascience.uci.edu
SUMMARY:Real Time Analytics @Twitter
DESCRIPTION:In this talk\, we will give an overview of real time analytics\, discuss the twitter real time data pipeline and how Storm is used for extracting analytics. We will also discuss the challenges we faced and lessons we have learned while building this infrastructure at Twitter. \n \nDescription: \n \nIn this talk\, we will give an overview of real time analytics\, discuss the twitter real time data pipeline and how Storm is used for extracting analytics. We will also discuss the challenges we faced and lessons we have learned while building this infrastructure at Twitter. \nDate: February 20\, 2015\nTime: 3:00 pm to 4:00 pm\nLocation: Donald Bren Hall\, Room 3011 (part of the ISG Seminar Series\nSpeaker: Dr. Karthik Ramasamy\, Twitter \nRSVP is not required. \nReal time analytics seems to be a buzz word these days. Twitter identified the need for real time analytics early on and invested in a massive data pipeline that collects\, aggregates\, processes large volumes of data in real time. At the heart of the pipeline is Twitter Storm\, a real-time stream processing engine widely used in Twitter. Storm is used for real-time data analytics\, time series aggregation\, and powering real-time features like trending topics. In this talk\, we will give an overview of real time analytics\, discuss the twitter real time data pipeline and how Storm is used for extracting analytics. We will also discuss the challenges we faced and lessons we have learned while building this infrastructure at Twitter. \nSPEAKER BIO:\nKarthik is the engineering manager and technical lead for Real Time Analytics at Twitter. He has two decades of experience working in parallel databases\, big data infrastructure and networking. He cofounded Locomatix\, a company that specializes in real timestreaming processing on Hadoop and Cassandra using SQL that was acquired by Twitter. Before Locomatix\, he had a brief stint with Greenplum where he worked on parallel query scheduling. Greenplum was eventually acquired by EMC for more than $300M. Prior to Greenplum\, Karthik was at Juniper Networks where he designed and delivered platforms\, protocols\, databases and high availability solutions for network routers that are widely deployed in the Internet. Before joining Juniper\, at the University of Wisconsin he worked extensively in parallel database systems\, query processing\, scale out technologies\, storage engine and online analytical systems. Several of those research results were spun out as a company later acquired by Teradata. Karthik is the author of several publications\, patents and one of the best selling book “Network Routing: Algorithms\, Protocols and Architectures.” He has a Ph.D. in Computer Science from UW Madison with a focus on databases.
URL:https://datascience.uci.edu/event/1710/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150209T160000
DTEND;TZID=America/Los_Angeles:20150209T171500
DTSTAMP:20260516T023443
CREATED:20160502T105708Z
LAST-MODIFIED:20160502T105708Z
UID:1707-1423497600-1423502100@datascience.uci.edu
SUMMARY:Privacy in the Land of Plenty
DESCRIPTION:Privacy-preserving data analysis has a large literature that spans several disciplines.  “Differential privacy” — a notion tailored to situations in which data are plentiful — has provided a theoretically sound and powerful framework\, and given rise to an explosion of research.  We will review the definition of differential privacy\, describe some algorithmic contributions\, and conclude with a surprising application. \n \nDescription: \n \nA presentation by Dr. Cynthia Dwork\, Microsoft Research \nTime: 4:00 p.m. – 5:15 p.m. \nWhere: Donald Bren Hall\, Room 6011 (directions) \n>> View event flyer. \nPrivacy-preserving data analysis has a large literature that spans several disciplines.  “Differential privacy” — a notion tailored to situations in which data are plentiful — has provided a theoretically sound and powerful framework\, and given rise to an explosion of research.  We will review the definition of differential privacy\, describe some algorithmic contributions\, and conclude with a surprising application. \nBio \nDr. Cynthia Dwork\, Distinguished Scientist at Microsoft Research\, is renowned for placing privacy-preserving data analysis on a mathematically rigorous foundation. A cornerstone of this work is differential privacy\, a strong privacy guarantee frequently permitting highly accurate data analysis. Dr. Dwork has also made seminal contributions in cryptography and distributed computing\, and is a recipient of the Edsger W. Dijkstra Prize\, recognizing some of her earliest work establishing the pillars on which every fault-tolerant system has been built for decades. She is a member of the National Academy of Sciences and the National Academy of Engineering\, and a Fellow of the American Academy of Arts and Sciences. \nFor more information\, please visit the UCI Department of Computer Science Distinguished Lecture Series.
URL:https://datascience.uci.edu/event/privacy-in-the-land-of-plenty/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150205T160000
DTEND;TZID=America/Los_Angeles:20150205T170000
DTSTAMP:20260516T023443
CREATED:20160502T105555Z
LAST-MODIFIED:20160502T105555Z
UID:1706-1423152000-1423155600@datascience.uci.edu
SUMMARY:Modeling wealth\, behavior\, and mobility with terabyte-scale network data
DESCRIPTION:A presentation by Joshua Blumenstock\, Assistant Professor\, Information School\, University of Washington\n \n \nDescription: \n\nA presentation by Joshua Blumenstock\, Assistant Professor\, Information School\, University of Washington\n \nDate: Thursday\, February 5\, 2015 \nTime: 4:00 – 5:00 p.m.\nLocation: Donald Bren Hall Room 4011 (directions)\nRSVP is not required\n \nCo-sponsored by the UCI Data Science Initiative (datascience.uci.edu) and the Center for Intelligent Systems and Machine Learning (cml.ics.uci.edu)\n \nAbstract:\nIn recent years\, the rapid proliferation of mobile phones in developing countries has provided billions of individuals with novel opportunities for social and economic interaction. Concurrently\, the data generated by mobile phone networks is enabling new data-intensive methods for studying the social and economic behavior of individuals in resource-constrained environments. After all\, these data reflect much more than simple communications activity: they capture the structure of social networks\, decisions about expenditures and consumption\, patterns of travel and mobility\, and the regularity of daily routines. In this talk\, I will discuss the results from two recent projects that derive behavioral insights from mobile phone data. The first study uses data on Mobile Money transfers in Rwanda and microeconomic models to better understand the motives that cause people to send money to friends and family in times of need. The second project combines call data with follow-up phone surveys to investigate the extent to which it is possible to predict an individual’s wealth and happiness based on his or her prior history of phone calls and several supervised learning models. These projects are enabled by generous support from the Institute for Money\, Technology\, and Financial Inclusion; Intel; the Gates Foundation; and the NSF.\n \nBio: \nJoshua Blumenstock is an Assistant Professor at the Information School\, an Adjunct Assistant Professor of Computer Science and Engineering\, and a founder of the Data Science and Analytics Lab at the University of Washington. His research develops theory and methods for the analysis of large-scale behavioral data\, with a focus on how such data can be used to better understand poverty and economic development. Recent projects combine field experiments with big spatiotemporal network data to model decision-making in poor and conflict-affected regions of the world. Prior to joining UW\, Joshua was a postdoc in the Department of Economics at Yale University. He has a Ph.D. in Information Science and a M.A. in Economics from U.C. Berkeley\, and Bachelor’s degrees in Computer Science and Physics from Wesleyan University. He is a recipient of the Intel Faculty Early Career Honor and a former fellow of the Thomas J. Watson Foundation and the Harvard Institutes of Medicine.
URL:https://datascience.uci.edu/event/modeling-wealth-behavior-and-mobility-with-terabyte-scale-network-data/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150130T150000
DTEND;TZID=America/Los_Angeles:20150130T160000
DTSTAMP:20260516T023443
CREATED:20160502T105432Z
LAST-MODIFIED:20160502T105432Z
UID:1705-1422630000-1422633600@datascience.uci.edu
SUMMARY:Large-Scale Machine Learning with the SimSQL System
DESCRIPTION:A presentation by Professor Chris Jermaine\, Computer Science\,  Rice University\n\n\n\n \nDescription: \n\nA presentation by Professor Chris Jermaine\, Computer Science\,  Rice University\n \nDate: Friday\, January 30\, 2015\nTime: 3:00 to 4:00 pm\nLocation: Donald Bren Hall\, Room 3011 (directions)\n\nIn this talk\, Professor Chris Jermaine will describe the SimSQL system\, which is a platform for writing and executing statistical codes over large data sets\, particularly for machine learning applications. Codes that run on SimSQL can be written in a very high-level\, declarative language called Buds. A Buds program looks a lot like a mathematical specification of an algorithm\, and statistical codes written in Buds are often just a few lines long.At its heart\, SimSQL is really a relational database system\, and like other relational systems\, SimSQL is designed to support data independence. That is\, a single declarative code for a particular statistical inference problem can be used regardless of data set size\, compute hardware\, and physical data storage and distribution across machines. One concern is that a platform supporting data independence will not perform well. But we’ve done extensive experimentation\, and have found that SimSQL performs as well as other competitive platforms that support writing and executing machine learning codes for large data sets.Bio:Chris Jermaine is an associate professor of computer science at Rice University. He is the recipient of an Alfred P. Sloan Foundation Research Fellowship\, a National Science Foundation CAREER award\, and an ACM SIGMOD Best Paper Award. In his spare time\, Chris enjoys outdoor activities such as hiking\, climbing\, and whitewater boating. In one particular exploit\, Chris and his wife floated a whitewater raft (home-made from scratch using a sewing machine\, glue\, and plastic) over 100 miles down the Nizina River (and beyond) in Alaska.
URL:https://datascience.uci.edu/event/large-scale-machine-learning-with-the-simsql-system/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150123T090000
DTEND;TZID=America/Los_Angeles:20150123T173000
DTSTAMP:20260516T023443
CREATED:20160502T104300Z
LAST-MODIFIED:20160502T104300Z
UID:1704-1422003600-1422034200@datascience.uci.edu
SUMMARY:Introduction to R Short Course
DESCRIPTION:This course provides an introduction to the fundamentals of the R language. \n\n \n\n\n \nDescription: \nThis course provides an introduction to the fundamentals of the R language. \nIn this course\, you will learn how to program in R and how to effectively use R to do data analysis. The course covers introduction to data/object types in R\, reading data into R\, creating data graphics\, accessing and installing R packages\, writing R functions\, fitting statistical models including regression models and performing statical tests as t-test\, and ANOVA. Practical examples will be provided during the course. \nDate: January 23\, 2015 \nTime: 9 a.m. to 5:30 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructors: Sepehr Akhavan and Homer Strong from the Department of Statistics\, UC Irvine. \nPre-requisites: 1) familiarity with basic statistical concepts\, and 2) basic programming knowledge. For the tutorial\, bring a laptop with R 3.1.2 and R Studio downloaded and installed. \n Application deadline is January 1\, 2015.
URL:https://datascience.uci.edu/event/1704/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150110T100000
DTEND;TZID=America/Los_Angeles:20150110T200000
DTSTAMP:20260516T023443
CREATED:20160427T143752Z
LAST-MODIFIED:20160427T143752Z
UID:1700-1420884000-1420920000@datascience.uci.edu
SUMMARY:Data Science Hackathon
DESCRIPTION:The UCI Data Science Initiative is hosting a Hackathon with the goal of analyzing data from Reddit\, a user-generated news platform. \nParticipants will work in teams to identify an interesting question and use data analytics to answer it. All teams will give a brief presentation at the end of the day for a chance to win a gift card. \n\n\n\n  \n \nThe UCI Data Science Initiative is hosting a Hackathon with the goal of analyzing data from Reddit\, a user-generated news platform. \nParticipants will work in teams to identify an interesting question and use data analytics to answer it. All teams will give a brief presentation at the end of the day for a chance to win a gift card. \nWhen: January 10\, 2015 from 10:00 a.m. to 8 p.m. (lunch and dinner will be provided).\nWho: Graduate students from all backgrounds are encouraged to participate\, though prior experience with programming or data analysis is preferred.\nWhere: Bren Hall\, Room 6011\, UC Irvine Campus\nWhy: Each member of the three winning teams will receive a $10 gift card. \nPossible tasks include: \n• Build models to predict which content will make it to the front page\n• Summarize and visualize Reddit data to show hidden aspects of the Reddit community\n• Rank users by influence\n• Anything else you can dream up! \nAbout Reddit\nReddit is an entertainment\, social networking\, and news website where registered community members can submit content\, such as posts or direct links. Only registered users can then vote submissions “up” or “down” to organize the posts and determine their position on the site’s pages. Reddit data is multifaceted and includes text\, URLs\, timestamps\, user IDs\, topics\, and popularity scores. \nDetails\nParticipants must bring there own computer with appropriate software downloaded. There are no restrictions on what programming language or tools you use (e.g. Python\, R\, Matlab). Each participant must register to attend the Hackathon. Participants may request a specific team during registration. Individual participants will be assigned a team the morning of the event. \nRegistration for the Data Science Initiative Hackathon is currently closed. Please contact datascience@uci.edu for further assistance. \n 
URL:https://datascience.uci.edu/event/data-science-hackathon-2/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20141117T090000
DTEND;TZID=America/Los_Angeles:20141117T170000
DTSTAMP:20260516T023443
CREATED:20160427T143553Z
LAST-MODIFIED:20160427T143553Z
UID:1699-1416214800-1416243600@datascience.uci.edu
SUMMARY:An Introduction to Linux on the High-Performance Computing Cluster
DESCRIPTION:This course is for researchers who have never used Linux and/or a compute cluster and introduces concepts and best practices for both. \nThe application deadline has passed.  \nTo find out about future one-day courses\, please join our mailing list. \n\n \n\n\n \nThis course is for researchers who have never used Linux and/or a compute cluster and introduces concepts and best practices for both. \nThe application deadline has passed.  \nTo find out about future one-day courses\, please join our mailing list. \nThis course covers how to best exploit the bash shell for both interactive work and batch jobs\, moving & simple manipulation of data\, as well very short introductions to programming in bash\, Perl\, and R. This is not computer science; this is a driver’s license. \nDate: November 17\, 2014\nTime: Tentatively scheduled from 9 a.m. to 5 p.m. with lunch provided\nLocation: Donald Bren Hall\, Room 3011\nInstructor: Harry Mangalam\, OIT/HPC\, UC Irvine\nPre-requisites: for the course\, none. For the tutorial\, a laptop with WiFi\, with a terminal application (Macs have the Terminal app; Windows need putty)\, and both would benefit from ‘x2go’.
URL:https://datascience.uci.edu/event/an-introduction-to-linux-on-the-high-performance-computing-cluster/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20141117T090000
DTEND;TZID=America/Los_Angeles:20141117T170000
DTSTAMP:20260516T023443
CREATED:20160427T143241Z
LAST-MODIFIED:20160427T143241Z
UID:1697-1416214800-1416243600@datascience.uci.edu
SUMMARY:Analyzing Data/BigData on Linux
DESCRIPTION:This course extends the topics introduced in the Introduction to Linux class\, including a lightning review of the introduction topics and then extending to large scale data processing. \nThe application deadline has passed.  \nTo find out about future one-day courses\, please join our mailing list. \n\n\n\n \nThis course extends the topics introduced in the Introduction to Linux class\, including a lightning review of the introduction topics and then extending to large scale data processing. \nThe application deadline has passed.  \nTo find out about future one-day courses\, please join our mailing list. \nIt covers using foreign data formats on Linux\, stream processing\, using efficient and appropriate file formats\, considerations for simple parallel processing\, introduction to different families of applications\, dealing with BigData sets. \nDate: November 18\, 2014\nTime: Tentatively scheduled from 9 a.m. to 5 p.m. with lunch provided\nLocation: Donald Bren Hall\, Room 3011\nInstructor: Harry Mangalam\, OIT/HPC\, UC Irvine\nPre-requisites: for the course\, the Introduction to Linux class or equivalent experience with Linux on a cluster. For the tutorial\, same as the Introduction to Linux class
URL:https://datascience.uci.edu/event/1697/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20141117T090000
DTEND;TZID=America/Los_Angeles:20141117T170000
DTSTAMP:20260516T023443
CREATED:20160427T142847Z
LAST-MODIFIED:20160427T142847Z
UID:1696-1416214800-1416243600@datascience.uci.edu
SUMMARY:Working with Big Data in Linux
DESCRIPTION:This course extends the topics introduced in the Introduction to Linux class\, including a lightning review of the introduction topics and then extending to large scale data processing. \nThe application deadline has passed.  \nTo find out about future one-day courses\, please join our mailing list. \n\n \n\n\n \nThis course extends the topics introduced in the Introduction to Linux class\, including a lightning review of the introduction topics and then extending to large scale data processing. \nThe application deadline has passed.  \nTo find out about future one-day courses\, please join our mailing list. \nIt covers using foreign data formats on Linux\, stream processing\, using efficient and appropriate file formats\, considerations for simple parallel processing\, introduction to different families of applications\, dealing with BigData sets. \nDate:October 12 \, 2015\nTime: Tentatively scheduled from 9 a.m. to 5 p.m. with lunch provided\nLocation: Donald Bren Hall\, Room 3011\nInstructor: Harry Mangalam\, OIT/HPC\, UC Irvine\nPre-requisites: for the course\, the Introduction to Linux class or equivalent experience with Linux on a cluster. For the tutorial\, same as the Introduction to Linux class
URL:https://datascience.uci.edu/event/working-with-big-data-in-linux/
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20141114T090000
DTEND;TZID=America/Los_Angeles:20141114T170000
DTSTAMP:20260516T023443
CREATED:20160427T142527Z
LAST-MODIFIED:20160427T142527Z
UID:1695-1415955600-1415984400@datascience.uci.edu
SUMMARY:Introduction to R
DESCRIPTION:This course provides an introduction to the fundamentals of the R language. \nThe application deadline has passed.  \nTo find out about future one-day courses\, please join our mailing list. \n \nThis course provides an introduction to the fundamentals of the R language. \nThe application deadline has passed.  \nTo find out about future one-day courses\, please join our mailing list.\nIn this course\, you will learn how to program in R and how to effectively use R to do data analysis. The course covers introduction to data/object types in R\, reading data into R\, creating data graphics\, accessing and installing R packages\, writing R functions\, fitting statistical models including regression models and performing statical tests as t-test\, and ANOVA. Practical examples will be provided during the course. \nDate: Friday\, November 14\, 2014 \nTime: Tentatively scheduled from 9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Sepehr Akhavan\, Department of Statistics\, UC Irvine \nPre-requisites: 1) familiarity with basic statistical concepts\, and 2) basic programming knowledge. For the tutorial\, bring a laptop with R downloaded and installed and WiFi. \n 
URL:https://datascience.uci.edu/event/introduction-to-r-2/
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