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X-ORIGINAL-URL:https://datascience.uci.edu
X-WR-CALDESC:Events for Data Science Initiative
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20161107T090000
DTEND;TZID=America/Los_Angeles:20161107T170000
DTSTAMP:20260508T211247
CREATED:20161008T164633Z
LAST-MODIFIED:20161008T164633Z
UID:1908-1478509200-1478538000@datascience.uci.edu
SUMMARY:Predictive Modeling with Python
DESCRIPTION:Learn about the use of predictive models in Python through scikit-learn.\n \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:November 7\, 2016 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Brian Vegetabile\, Eric Nalisnick\, Christine Lee\, UC Irvine \nPre-requisites: 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. \nLearning Materials:https://github.com/UCIDataScienceInitiative/PredictiveModeling_withPython
URL:https://datascience.uci.edu/event/predictive-modeling-with-python-4/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=image/png:https://datascience.uci.edu/wp-content/uploads/2014/09/python_2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20161025T090000
DTEND;TZID=America/Los_Angeles:20161025T170000
DTSTAMP:20260508T211247
CREATED:20161008T164036Z
LAST-MODIFIED:20161008T164036Z
UID:1907-1477386000-1477414800@datascience.uci.edu
SUMMARY:A Deep Introduction to Julia
DESCRIPTION:This workshop aims to introduce both users of scripting languages and advanced programmers to the Julia ecosystem and explore details about the Julia language which can help produce efficient and readable code.\n \nThe goal of the workshop is for students to understand where Julia can be applied and be well-equipped to start using Julia in their own research. Students will learn about the current state of Julia development (IDEs\, documentation\, where to get help)\, how to write efficient code by understanding some of Julia’s internals via small projects\, solve problems using advanced Julia features (metaprogramming\, multiple-dispatch\, etc.)\, and learn workarounds to common issues newcomers face (scoping problems\, type conversions\, etc.). \nDate:October 25\, 2016 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Chris Rackauckas\, UC Irvine \nPre-requisites: Solid understanding of programming. Installing Julia beforehand is not required\, though highly recommended. Attendees may wish to install the Julia/Atom IDE before the workshop\, though be advised this may not be easy (instructions). Help for doing so can be found at the UCI Data Science Initiative Gitter and the JunoLab Gitter. \nMaterials Repository:https://github.com/UCIDataScienceInitiative/IntroToJulia
URL:https://datascience.uci.edu/event/a-deep-introduction-to-julia/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2014/12/DSC00932.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20161020T090000
DTEND;TZID=America/Los_Angeles:20161020T170000
DTSTAMP:20260508T211247
CREATED:20161008T162053Z
LAST-MODIFIED:20161008T162053Z
UID:1906-1476954000-1476982800@datascience.uci.edu
SUMMARY:Next Generation Sequencing Data Analysis
DESCRIPTION:This course provides an introduction to the basics of using open source software tools to analyze large scale genomics data\, specifically Next Generation Sequencing data.\n \nIn this course\, you will learn general NGS workflow\, data analysis pipeline\, data formats\, short read mapping and alignment software\, general workflow for DNA-seq\, RNA-seq and ChIP-seq. Data visualization tools will also be covered. Single Cell RNA-seq will be discussed. \nDate:October 20\, 2016 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Jenny Wu\, UC Irvine \nPre-requisites: Some programming experience is recommended. For the tutorial\, bring a laptop with wifi and is pre-registered with the UCI Mobile network with a free HPC account. For Windows users\, please install the “putty” terminal program (PuTTY) and the Windows x2go client (x2go). \nLearning Materials:http://ghtf.biochem.uci.edu/content/ngs-data-analysis
URL:https://datascience.uci.edu/event/next-generation-sequencing-data-analysis/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20161014T160000
DTEND;TZID=America/Los_Angeles:20161014T170000
DTSTAMP:20260508T211247
CREATED:20161006T130029Z
LAST-MODIFIED:20161006T130029Z
UID:1895-1476460800-1476464400@datascience.uci.edu
SUMMARY:Information Session on the Insight Data Science Fellows Program
DESCRIPTION:Information Session on the Insight Data Science Fellows Program\nSpeaker: Emily Thompson\, Insight Data Science\,  insightdatascience.com\nTarget audience: Graduate students and Postdoctoral researchers \nDate: Friday\, October 14\nTime: 4:00 to 5:00 p.m.\nLocation: Calit2 Auditorium\, UCI (directions) \n[button link=”https://uci-oai.formstack.com/forms/insight_info_101416″ color=”default” size=”” stretch=”” type=”” shape=”” target=”_self” title=”” gradient_colors=”|” gradient_hover_colors=”|” accent_color=”” accent_hover_color=”” bevel_color=”” border_width=”1px” icon=”” icon_divider=”yes” icon_position=”left” modal=”” animation_type=”0″ animation_direction=”down” animation_speed=”0.1″ animation_offset=”” alignment=”left” class=”” id=””]RSVP Today[/button][separator style_type=”none” top_margin=”10″ bottom_margin=”10″ sep_color=”” border_size=”” icon=”” icon_circle=”” icon_circle_color=”” width=”” alignment=”center” class=”” id=””]\nAbout Emily Thompson \nEmily Thompson\, Product Manager at Insight Data Science\, will be talking about her transition from academia to the field of data science. She will also be leading a discussion on careers in data science\, health data science\, and data engineering\, and steps you can take towards these career paths. Top companies are hiring data scientists and engineers to help find insights in the petabytes of data that they collect every day. Scientists and engineers from diverse fields\, including computational biology\, neuroscience\, math\, and statistics are playing key roles in transforming the way of working with data to impact our daily lives. \n  \nAbout Insight Fellows Program\nThe Insight Fellows Program is a training fellowship designed to bridge the gap between academia and a career in data science or data engineering. Insight provides seven-week\, full-time\, training fellowships in Silicon Valley\, New York\, and Boston. We offer a full tuition scholarship\, dedicated office space\, and project-based learning under the guidance of top industry mentors. Over 600 Insight alumni are now working at Facebook\, Apple\, LinkedIn\, Twitter\, Reddit\, Netflix\, NBC\, MTV\, Khan Academy\, Biogen and other top companies.\nIn this info session\, we will provide a high-level overview of data science and engineering in industry and describe the Insight Fellows Program. The Program is open to individuals from all subjects working on quantitative data analysis (our Data Science and Health Data Science Programs have a PhD requirement). The session will also include time for Q&A. Learn more at: insightdatascience.com
URL:https://datascience.uci.edu/event/information-session-on-the-insight-data-science-fellows-program-2/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20161007T150000
DTEND;TZID=America/Los_Angeles:20161007T183000
DTSTAMP:20260508T211247
CREATED:20160912T105453Z
LAST-MODIFIED:20160912T105453Z
UID:1871-1475852400-1475865000@datascience.uci.edu
SUMMARY:Symposium on Recent Advances in Data Science
DESCRIPTION:The Data Science Initiative is sponsoring a short symposium highlighting recent advances in data science. The event will consist of talks on a variety of topics related to data science followed by a reception and graduate student poster session\, including posters from the 2016 Data Science Summer Fellows. \nDate: October 7\, 2016\nTime: 3:00 – 6:30 p.m.\nLocation: Calit2 Auditorium (directions) \nThis event has passed. \n>> View photos from the event. \nSchedule: \n\n\n\n\nTime\nDescription\n\n\n\n\n3:00 p.m.\nUpdate on data science at UCI\nPadhraic Smyth\, Director\, UCI Data Science Initiative\n\n\n3:20 p.m.\nResearch Highlight: Interpreting machine learning predictions\nSameer Singh\, Assistant Professor\, Department of Computer Science\, UCI\n\n\n3:50 p.m.\nResearch Highlight:  A Bayesian perspective on publication bias in the Reproducibility Project: Psychology\nJoachim Vandekerckhove\, Associate Professor\, Department of Cognitive Sciences\, UCI\n\n\n4:20 p.m.\nInvited Keynote: Google data and public sentiment\nDr. Steve Scott\, Senior Economic Analyst\, Google\n\n\n5:00 p.m.\nReception and graduate student poster session\n\n\n\n\n[/two_third][one_third spacing=”yes” last=”yes” center_content=”no” hide_on_mobile=”no” background_color=”” background_image=”” background_repeat=”no-repeat” background_position=”left top” link=”” hover_type=”none” border_position=”all” border_size=”0px” border_color=”” border_style=”solid” padding=”” margin_top=”” margin_bottom=”” animation_type=”0″ animation_direction=”down” animation_speed=”0.1″ animation_offset=”” class=”” id=””] \n \nSubmit a Poster\nUCI graduate students are invited to submit applications to present at the poster session on current and research projects that have a significant data science component\, e.g.\, research in areas such as machine learning\, statistics\, databases\, data privacy\, visualization as well as research on domain-specific topics in the sciences and humanities that have a strong data-driven component. \n  \nDeadline has passed[/one_third]
URL:https://datascience.uci.edu/event/academic-year-kickoff-event-advances-in-data-science/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20161007T090000
DTEND;TZID=America/Los_Angeles:20161007T170000
DTSTAMP:20260508T211247
CREATED:20160929T151518Z
LAST-MODIFIED:20160929T151518Z
UID:1887-1475830800-1475859600@datascience.uci.edu
SUMMARY:Introduction to R
DESCRIPTION:This course provides an introduction to the fundamentals of the R language and its applications to data analysis. \n \nIn this course\, you will learn how to program in R and how to effectively use R for data analysis. The course covers an introduction to data/object types in R\, reading data\, creating data visualizations\, accessing and installing R packages\, writing R functions\, fitting statistical models including regression models and performing statistical tests including t-tests and ANOVA. Practical examples will be provided during the course. \nDate:October 7\, 2016 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Emma Smith\, Steven Brownlee\, 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. \nTeaching material repository:https://github.com/UCIDataScienceInitiative/IntroR_Workshop
URL:https://datascience.uci.edu/event/introduction-to-r-8/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20161003T090000
DTEND;TZID=America/Los_Angeles:20161003T170000
DTSTAMP:20260508T211247
CREATED:20160926T183542Z
LAST-MODIFIED:20160926T183542Z
UID:1877-1475485200-1475514000@datascience.uci.edu
SUMMARY:Intro to Linux on the HPC
DESCRIPTION:This course is for researchers who have never used Linux and/or a computer cluster and introduces concepts and best practices for both. \n \nDescription: \n \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 3\, 2016\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’. \n 
URL:https://datascience.uci.edu/event/intro-to-linux-on-the-hpc/
LOCATION:Donald Bren Hall 3011
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2014/09/8450190120_ebcf41863d_o.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160531T090000
DTEND;TZID=America/Los_Angeles:20160531T170000
DTSTAMP:20260508T211247
CREATED:20160601T125224Z
LAST-MODIFIED:20160601T125224Z
UID:1778-1464685200-1464714000@datascience.uci.edu
SUMMARY:Introduction to R
DESCRIPTION:This course provides an introduction to the fundamentals of the R language and its applications to data analysis. \nDate:May 31\, 2016 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 3011 \n \nIn this course\, you will learn how to program in R and how to effectively use R for data analysis. The course covers introduction to data/object types in R\, reading data\, creating data visualizations\, accessing and installing R packages\, writing R functions\, fitting statistical models including regression models and performing statistical tests including t-tests and ANOVA. Practical examples will be provided during the course. \nDate:May 31\, 2016 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 3011 \nInstructor: Emma Smith\, Wen He\, 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. \nTeaching material repository:https://github.com/UCIDataScienceInitiative/IntroR_Workshop
URL:https://datascience.uci.edu/event/introduction-to-r-7/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160524T090000
DTEND;TZID=America/Los_Angeles:20160524T130000
DTSTAMP:20260508T211247
CREATED:20160518T104716Z
LAST-MODIFIED:20160518T104716Z
UID:1769-1464080400-1464094800@datascience.uci.edu
SUMMARY:Introduction to Julia
DESCRIPTION:This workshop aims to introduce both users of scripting languages and advanced programmers to the Julia ecosystem\, and explore details about the Julia language which can help produce efficient and readable code. \n \nThis workshop aims to introduce both users of scripting languages and advanced programmers to the Julia ecosystem\, and explore details about the Julia language which can help produce efficient and readable code. \nTo find out about future one-day courses\, please join our mailing list. \nJulia is a relatively new language which has been making waves in the scientific community due to its ease of use as a scripting language combined with its ability to produce programs with comparable runtimes to C/Fortran. For this reason\, Julia is situated is fast becoming a major language in high-performance and “big data” computing. However\, the current state of the Julia ecosystem can be intimidating to much of its target audience\, which is both high-level scriptors (using MATLAB/Python/R) all the way to developers of high-performance libraries (using C/Fortran). This workshop is aims to introduce both users of scripting languages and advanced programmers to the Julia ecosystem\, and explore details about the Julia language which can help produce efficient and readable code. The goal of the workshop is for students to understand where Julia can be applied and be well-equipped to start using Julia in their own research. Students will learn about the current state of Julia development (IDEs\, documentation\, where to get help)\, how to write efficient code by understanding some of Julia’s internals via small projects\, solve problems using advanced Julia features (metaprogramming\, multiple-dispatch\, etc.)\, and learn workarounds to common issues newcomers face (scoping problems\, type conversions\, etc.). Near the end of the workshop\, participants will break out into groups to solve problems which mirror research problems in data science and scientific computing. \nDate:May 24\, 2016\nTime: 9 am to 1 pm\nLocation: Donald Bren Hall\, Room 3011\nInstructor: Chris Rackauckas\nPre-requisites: Previous experience with a scripting language (R/Python/MATLAB etc.). \n\n\n\n\n\n\n\nRegistration for this workshop has closed due to capacity constraints. You my still attend as a walk-in on the day of the workshop\, as long as there are seats available when the workshop begins. You are also invited to fill out this workshop interest survey. \n\n\n\n\nName* \n\nFirst Name*\nLast Name*\n\n \n\n\n\nEmail*\n\n\n\n\nCheckbox*\n\nIntroduction to LinuxIntroduction to RAnalyzing Big Data with LinuxSoftware CarpentryAdvanced RPredictive Modeling with PythonIntroduction to StanIntroduction to Next Generation Sequencing \nOther:Other Value\n\n\n\n\n\n\n\n \n \n\n\n\nContact Form Generator
URL:https://datascience.uci.edu/event/introduction-to-julia/
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2014/09/Big_Data_DARPA.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160520T090000
DTEND;TZID=America/Los_Angeles:20160520T200000
DTSTAMP:20260508T211247
CREATED:20160503T115919Z
LAST-MODIFIED:20160503T115919Z
UID:1714-1463734800-1463774400@datascience.uci.edu
SUMMARY:Southern California Machine Learning Symposium
DESCRIPTION:  \nClick to expand\n  \nThe Southern California Machine Learning Symposium will be held in the UCI Student Center on Friday May 20th. The symposium will include morning and afternoon sessions on the latest advances in machine learning with invited talks from leading researchers in the field. The event will also include a student poster session and a reception in the evening. \n\nFull details and registration information are available at \nhttp://www.calit2.uci.edu/calit2-events/calendar.aspx?eid=796\n \nThis event is co-sponsored by the UCI Data Science Initiative\, the UCI Institute for Genomics and Bioinformatics\, the UCI Center for Machine Learning and Intelligent Systems\, and Calit2 Irvine.
URL:https://datascience.uci.edu/event/southern-california-machine-learning-symposium-2/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160514T140000
DTEND;TZID=America/Los_Angeles:20160515T200000
DTSTAMP:20260508T211247
CREATED:20160420T103549Z
LAST-MODIFIED:20160420T103549Z
UID:1672-1463234400-1463342400@datascience.uci.edu
SUMMARY:UCI Data Hackathon
DESCRIPTION:Come join the UCI Data Hackathon\, May 14-15th. This hackathon will award cash prizes to teams working in 3 challenge areas: data visualization\, machine learning\, and app development. \nDate: May 14-15\nTime: 2:00pm – 8:00pm\nLocation: Google Hangout\nMore Info Here\n 
URL:https://datascience.uci.edu/event/1672/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160506T120000
DTEND;TZID=America/Los_Angeles:20160506T170000
DTSTAMP:20260508T211247
CREATED:20160505T115413Z
LAST-MODIFIED:20160505T115413Z
UID:1757-1462536000-1462554000@datascience.uci.edu
SUMMARY:Fostering Literacy with Text and Data Mining
DESCRIPTION:Symposium:  Fostering Literacy and Learning with Text and Data Mining \n \nDescription: \n \nSymposium:  Fostering Literacy and Learning with Text and Data Mining \nDate: Friday\, May 6\, 2016\nTime: 12:00 – 5:00 p.m.\nLocation: UC Irvine\, Calit2 Auditorium (directions) \n\n \nVisit the Fostering Literacy and Learning with Text and Data Mining event page for a list of speakers and more information.\n \nThis event is free and open to the public. RSVP is requested. \nPlease complete the form below to RSVP. \n \n \n\n\n\n\n\nFostering Literacy and Learning with Text and Data Mining RSVP\n\n\n\nName* \n\nFirst Name*\nLast Name*\n\n \n\n\n\nEmail*\n\n\nUCNetID (if applicable)\n\n\n\n\nPosition\n\nPhD StudentMasters StudentUndergraduate StudentPostdoctoral FellowResearcherFacultyStaff \nOther:\n\n\n\n\n\n\n\nAre you affiliated with UCI?*\nYesNo\n\n\n\n\n\n\nHow did you find out about this event? (optional)\n\nData Science Initiative WebsiteFacebookToday.uci.eduData Science e-newsletter \nOther:
URL:https://datascience.uci.edu/event/fostering-literacy-with-text-and-data-mining-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160506T120000
DTEND;TZID=America/Los_Angeles:20160506T170000
DTSTAMP:20260508T211247
CREATED:20160420T103306Z
LAST-MODIFIED:20160420T103306Z
UID:1670-1462536000-1462554000@datascience.uci.edu
SUMMARY:Fostering Literacy with Text and Data Mining
DESCRIPTION:Symposium:  Fostering Literacy and Learning with Text and Data Mining \nDate: Friday\, May 6\, 2016\nTime: 12:00 – 5:00 p.m.\nLocation: UC Irvine\, Calit2 Auditorium (directions) \n \n\n \nVisit the Fostering Literacy and Learning with Text and Data Mining event page for a list of speakers and more information.\n \nThis event is free and open to the public. RSVP is requested. \nPlease complete the form below to RSVP.
URL:https://datascience.uci.edu/event/fostering-literacy-with-text-and-data-mining/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160506T090000
DTEND;TZID=America/Los_Angeles:20160506T170000
DTSTAMP:20260508T211247
CREATED:20160505T115059Z
LAST-MODIFIED:20160505T115059Z
UID:1756-1462525200-1462554000@datascience.uci.edu
SUMMARY:Advanced 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. \nThis half-day course builds upon Predictive Modeling with Python\, covering best practices for building predictive models that perform well in noisy\, real-world domains. Feature learning/engineering and model ensembling will be the focus of the course. Examples and exercises will be implemented using iPython notebooks and the SciKit-Learn library. Participants must have taken Predictive Modeling with Python or obtain instructor permission to enroll. \nThis course is targeted primarily at graduate students who have not already taken a full course in machine learning. \nDate:May 6\, 2016 \nTime: 9 a.m. to 1 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructors:Eric Nalisnick \nPrerequisites: Predictive Modeling with Python or equivalent. To understand the more theoretical aspects of the course\, it is recommended to have knowledge of linear algebra\, probability\, and calculus. \nFor course materials and more information\, check out the \nsoon-to-be-updated Workshop page. \n 
URL:https://datascience.uci.edu/event/advanced-predictive-modeling-with-python/
ATTACH;FMTTYPE=image/png:https://datascience.uci.edu/wp-content/uploads/2014/09/python_2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160422T090000
DTEND;TZID=America/Los_Angeles:20160422T170000
DTSTAMP:20260508T211247
CREATED:20160505T114945Z
LAST-MODIFIED:20160505T114945Z
UID:1755-1461315600-1461344400@datascience.uci.edu
SUMMARY:Introduction to R
DESCRIPTION:This course provides an introduction to the fundamentals of the R language and its applications to data analysis. \n \nDescription: \n \nThis course provides an introduction to the fundamentals of the R language and its applications to data analysis. \nIn this course\, you will learn how to program in R and how to effectively use R for data analysis. The course covers introduction to data/object types in R\, reading data\, creating data visualizations\, accessing and installing R packages\, writing R functions\, fitting statistical models including regression models and performing statistical tests including t-tests and ANOVA. Practical examples will be provided during the course. \nDate:April 22\, 2016 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Sepehr Akhavan (Department of Statistics)\, Homer Strong (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. \nTeaching material repository:https://github.com/UCIDataScienceInitiative/IntroR_Workshop \n  \n\n\n\n\n\n\n\nRegistration for this workshop has closed due to capacity constraints. You my still attend as a walk-in on the day of the workshop\, as long as there are seats available when the workshop begins. You are also invited to fill out this workshop interest survey. \n\n\n\n\nName* \n\nFirst Name*\nLast Name*\n\n \n\n\n\nEmail*\n\n\n\n\nCheckbox*\n\nIntroduction to LinuxIntroduction to RAnalyzing Big Data with LinuxSoftware CarpentryAdvanced RPredictive Modeling with PythonIntroduction to StanIntroduction to Next Generation Sequencing \nOther:Other Value\n\n\n\n\n\n\n\n \n \n\n\n\nContact Form Generato
URL:https://datascience.uci.edu/event/introduction-to-r-6/
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:20160422T090000
DTEND;TZID=America/Los_Angeles:20160422T170000
DTSTAMP:20260508T211247
CREATED:20160125T142933Z
LAST-MODIFIED:20160125T142933Z
UID:1428-1461315600-1461344400@datascience.uci.edu
SUMMARY:Introduction to R
DESCRIPTION:This course provides an introduction to the fundamentals of the R language and its applications to data analysis. \nDate:April 22\, 2016 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \n \nIn this course\, you will learn how to program in R and how to effectively use R for data analysis. The course covers introduction to data/object types in R\, reading data\, creating data visualizations\, accessing and installing R packages\, writing R functions\, fitting statistical models including regression models and performing statistical tests including t-tests and ANOVA. Practical examples will be provided during the course. \nDate:April 22\, 2016 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Sepehr Akhavan (Department of Statistics)\, Homer Strong (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. \nTeaching material repository:https://github.com/UCIDataScienceInitiative/IntroR_Workshop
URL:https://datascience.uci.edu/event/introduction-to-r/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160414T090000
DTEND;TZID=America/Los_Angeles:20160414T170000
DTSTAMP:20260508T211247
CREATED:20160505T114820Z
LAST-MODIFIED:20160505T114820Z
UID:1754-1460624400-1460653200@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: April 14\, 2016 \nTime: 9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 3011 \nInstructors: Kevin Bache\, Eric Nalisnick\, Brian Vegetabile\, Christine Lee \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. \nFor course materials and more information\, check out the \nGitHub repository \n 
URL:https://datascience.uci.edu/event/predictive-modeling-with-python-3/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160407T090000
DTEND;TZID=America/Los_Angeles:20160407T170000
DTSTAMP:20260508T211247
CREATED:20160505T114711Z
LAST-MODIFIED:20160505T114711Z
UID:1753-1460019600-1460048400@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:April 7\, 2016\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-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160329T090000
DTEND;TZID=America/Los_Angeles:20160329T170000
DTSTAMP:20260508T211247
CREATED:20160505T114602Z
LAST-MODIFIED:20160505T114602Z
UID:1752-1459242000-1459270800@datascience.uci.edu
SUMMARY:Introduction to Linux
DESCRIPTION:This course is for researchers who have never used Linux and/or a computer 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 computer 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:March 29\, 2016\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’. \n\n\n\n\n\n\n\nRegistration for this workshop has closed due to capacity constraints. You my still attend as a walk-in on the day of the workshop\, as long as there are seats available when the workshop begins. You are also invited to fill out this workshop interest survey. \n\n\n\n\nName* \n\nFirst Name*\nLast Name*\n\n \n\n\n\nEmail*\n\n\n\n\nCheckbox*\n\nIntroduction to LinuxIntroduction to RAnalyzing Big Data with LinuxSoftware CarpentryAdvanced RPredictive Modeling with PythonIntroduction to StanIntroduction to Next Generation Sequencing \nOther:Other Value\n\n\n\n\n\n\n\n \n \n\n\n\nContact Form Generator
URL:https://datascience.uci.edu/event/introduction-to-linux-4/
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:20160304T090000
DTEND;TZID=America/Los_Angeles:20160304T170000
DTSTAMP:20260508T211247
CREATED:20160505T114339Z
LAST-MODIFIED:20160505T114339Z
UID:1751-1457082000-1457110800@datascience.uci.edu
SUMMARY:Topics in R: Shiny\, RMarkdown\, Tidy Data
DESCRIPTION:This workshop covers several advanced topics in R\, including building web apps with Shiny and reports with RMarkdown. \n \nDescription: \n \nThis workshop covers several advanced topics in R\, including building web apps with Shiny and reports with RMarkdown. \nThis workshop covers RMarkdown and will include composing an analyses in an RMarkdown document. There will be an introduction to the Shiny library for building web applications and dashboards. Other tentative topics include “tidying” data with tidyr\, dplyr\, and plyr\, and debugging methods. The class requires proficiency in R programming. \nDate: March 4\, 2015 \nTime:9am to 5 pm \nLocation: Donald Bren Hall\, Room 2011 \nInstructor: Sepehr Akhavan\, Homer Strong\, Lingge Li Department of Statistics\, and Susan Duncan\, Department of Cognitive Science\,UC Irvine \nPre-requisites:Introduction to R\, or equivalent experience. \nTo find out about future one-day courses\, please join our mailing list. \n 
URL:https://datascience.uci.edu/event/topics-in-r-shiny-rmarkdown-tidy-data/
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:20160211T090000
DTEND;TZID=America/Los_Angeles:20160211T170000
DTSTAMP:20260508T211247
CREATED:20160505T114212Z
LAST-MODIFIED:20160505T114212Z
UID:1750-1455181200-1455210000@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. \n \nDescription: \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. \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:February 11\, 2016\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-2/
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2014/09/Big_Data_DARPA.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160205T100000
DTEND;TZID=America/Los_Angeles:20160205T173000
DTSTAMP:20260508T211247
CREATED:20160505T114024Z
LAST-MODIFIED:20160505T114024Z
UID:1749-1454666400-1454693400@datascience.uci.edu
SUMMARY:Data Science and Digital Humanities
DESCRIPTION:Digital Humanities encompass a variety of topics\, from curating online collections to data mining large cultural data sets. \n \nDescription: \n \nDigital Humanities encompass a variety of topics\, from curating online collections to data mining large cultural data sets. \nPlease join us for a 1-day symposium where leading scholars will present and discuss hands-on digital humanities projects both in terms of their conceptual research design and of their infrastructure. \nDate: February 5\, 2016\nTime: 10:00 a.m. – 5:30 p.m.\nEvent Location: Calit2 Auditorium (directions and parking information) \n\nREGISTER \n\n \nThis event is free and open to the public.\nInvite your friends. \nOrganized by Peter Krapp and Geoffrey Bowker.\nCo-sponsored by the Data Science Initiative and the Digital Humanities Working Group at the Humanities Commons \nDigital Humanities practices incorporate both digitized and born-digital materials and combine methodologies from humanities disciplines (e.g. history\, philosophy\, linguistics\, literary criticism\, art history) with tools provided by computing (data visualization\, data mining\, statistics\, computational analysis) and digital publishing. These areas of research\, teaching\, and creation at the intersection of computing and the humanities receive attention and grant funding\, but are rarely discussed in terms of institutional support. Developing from what used to be called humanities computing\, Digital Humanities encompass a variety of topics\, from curating online collections to data mining large cultural data sets\, but there are still observers who feel that its practices are not “humanities” as such. Introducing the question of technology into the humanities shifts the focus to networks of technologies and institutions that allow a given culture to select\, store\, and process relevant data\, but also invites an intervention in the interstice between academic practices\, for instance in supplementing spatial models (writing\, graphs\, illustrations) with time-based modeling (videos\, interactive models) of those data. \nFor more information\, please visit the UCI Humanities Commons website. \n  \n\n\n\n\nTime\nPresenter\nTalk Title\n\n\n\n\n10:00 a.m.\nPeter Krapp\nProfessor\, Film & Media Studies\nSchool of Humanities\, UCI\nWelcome and Introductions\n\n\n10:30 a.m.\nKatherine D. Harris\nAssociate Professor Department of English & Comparative Literature\, San Jose State University\nUsing Bootstrap Digital Humanities to Explore Topic Modeling: Ghosts\, Haunted Houses\, and Heroines in 19th-Century Literature\n\n\n11:00 a.m.\nScott Kleinman\nProfessor of English & Director\, Center for the Digital Humanities\, California State University Northridge\nDigital Humanities Projects with Small and Unusual Data: Some Experiences from the Trenches\n\n\n11:30 a.m.\nDiscussion\n \n\n\n 12:00 p.m.\nBreak \n \n\n\n1:00 p.m.\nKathi Berens\nAssistant Professor of Digital Humanities and Publishing\, Portland State University\nLiterary/Ludic Reading: Is there a Feminist Poetics of Interface?\n\n\n1:30 p.m.\nMaria Pantelia\nProfessor of Classics & Director of the Thesaurus Linguae Graecae\, UC Irvine\nThe Future of the Past: The Thesaurus Linguae Graecae Project\n\n\n\n2:00 p.m.\nDiscussion\n \n\n\n2:30 p.m.\nBreak \n \n\n\n3:00 p.m.\nJeremy Douglass\nAssistant Professor of English\, UC Santa Barbara\n Graphs in the clouds: DH infrastructure for structured narrative\n\n\n3:30 p.m.\nDavid Bamman\nAssistant Professor\, School of Information\, UC Berkeley\nNatural Language Processing for the Long Tail\n\n\n4:00 p.m.\nMiriam Posner\nCoordinator and Core Faculty\, Digital Humanities Program\, UCLA\nMoney and Time: Some Hard Truths about Institutional Support for Digital Humanities\n\n\n4:30 p.m.\nDiscussion\n \n\n\n5:00 p.m.\nConclusion\n \n\n\n\n\n  \n\nREGISTER
URL:https://datascience.uci.edu/event/data-science-and-digital-humanities-2/
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160205T100000
DTEND;TZID=America/Los_Angeles:20160205T170000
DTSTAMP:20260508T211247
CREATED:20160125T143045Z
LAST-MODIFIED:20160125T143045Z
UID:1430-1454666400-1454691600@datascience.uci.edu
SUMMARY:Data Science and Digital Humanities
DESCRIPTION:Please join us for a 1-day Symposium where leading scholars will present and discuss hands-on digital humanities projects both in terms of their conceptual research design and of their infrastructure. Developing from what used to be called humanities computing\, Digital Humanities encompass a variety of topics\, from curating online collections to data mining large cultural data sets\, but there are still observers who feel that its practices are not “humanities” as such.
URL:https://datascience.uci.edu/event/data-science-and-digital-humanities/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160129T090000
DTEND;TZID=America/Los_Angeles:20160129T170000
DTSTAMP:20260508T211247
CREATED:20160505T113902Z
LAST-MODIFIED:20160505T113902Z
UID:1748-1454058000-1454086800@datascience.uci.edu
SUMMARY:Introduction to R
DESCRIPTION:This course provides an introduction to the fundamentals of the R language and its applications to data analysis. \n \nDescription: \n \nThis course provides an introduction to the fundamentals of the R language and its applications to data analysis. \nIn this course\, you will learn how to program in R and how to effectively use R for data analysis. The course covers introduction to data/object types in R\, reading data\, creating data visualizations\, accessing and installing R packages\, writing R functions\, fitting statistical models including regression models and performing statistical tests including t-tests and ANOVA. Practical examples will be provided during the course. \nDate:January 29\, 2016 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Sepehr Akhavan (Department of Statistics)\, Homer Strong (Department of Statistics)\,Emily Smith (Department of Sociology)\, Eric Lai (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. \nTeaching material repository:https://github.com/UCIDataScienceInitiative/IntroR_Workshop
URL:https://datascience.uci.edu/event/introduction-to-r-5/
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:20160122T090000
DTEND;TZID=America/Los_Angeles:20160122T170000
DTSTAMP:20260508T211247
CREATED:20160505T112953Z
LAST-MODIFIED:20160505T112953Z
UID:1747-1453453200-1453482000@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:January 22\, 2016 \nTime: 9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructors: Kevin Bache\, Eric Nalisnick\, Brian Vegetabile\, Christine Lee \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. \nFor course materials and more information\, check out the \nGitHub repository
URL:https://datascience.uci.edu/event/predictive-modeling-with-python-2/
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:20160108T090000
DTEND;TZID=America/Los_Angeles:20160108T170000
DTSTAMP:20260508T211247
CREATED:20160505T112823Z
LAST-MODIFIED:20160505T112823Z
UID:1746-1452243600-1452272400@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:January 8th\, 2016\nTime: 9 a.m. to 5 p.m. with lunch provided\nLocation: Donald Bren Hall\, Room 4011\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-3/
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:20151203T090000
DTEND;TZID=America/Los_Angeles:20151203T170000
DTSTAMP:20260508T211247
CREATED:20160505T112507Z
LAST-MODIFIED:20160505T112507Z
UID:1745-1449133200-1449162000@datascience.uci.edu
SUMMARY:Big Data Methods: Hadoop\, Spark\, and more
DESCRIPTION:Expert instructors from the San Diego Supercoming Center (SDSC) will visit to teach a 1-day tutorial on the latest big data technologies. These include general tools such as Hadoop and Spark\, as well as the resources such as Comet which are available at SDSC. \n \nDescription: \nExpert instructors from the San Diego Supercoming Center (SDSC) will visit to teach a 1-day tutorial on the latest big data technologies. These include general tools such as Hadoop and Spark\, as well as the resources such as Comet which are available at SDSC. \nTo find out about future one-day courses\, please join our mailing list. \nTopics will include:\n– Hadoop & MapReduce\n– Spark\n– MongoDB and other databases\n– Graph databases such as Neo4j \nInstructors (all from SDSC): Mahidha Tatineni has a Ph.D in Aerospace Engineering and leads the User Services group\, Amarnath Gupta has a Ph.D in Computer Science and leads the Advanced Query Processing Lab\, and Andrea Zonca has a Ph.D in Astrophysics and is a computational scientist at SDSC. \nPrerequisites: Familiarity with Linux & python. \n9-10:30 Intro to SDSC\, Comet\, Big Data and other resources at SDSC\n10:30-10:45 break\n10:45-12:15 Introduction to general Big Data techniques\n12:15-1 Lunch\n1-2:30 Introductory Spark for Scientific Computing with Python\n2:30-2:45 break\n2:45-4:30 Advanced Spark for Scientific Computing\n4:30-5 Wrap-up\, Q&A \nDate:December 3\, 2015\nTime: Tentatively scheduled from 9 a.m. to 5 p.m. with lunch provided\nLocation: Donald Bren Hall\, Room 3011\nInstructors:Mahidha Tatineni\, Amarnath Gupta\, Andrea Zonca\, San Diego Supercomputer Center (SDSC)\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/big-data-methods-hadoop-spark-and-more/
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2014/09/Big_Data_DARPA.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20151124T090000
DTEND;TZID=America/Los_Angeles:20151124T170000
DTSTAMP:20260508T211247
CREATED:20160505T112244Z
LAST-MODIFIED:20160505T112244Z
UID:1744-1448355600-1448384400@datascience.uci.edu
SUMMARY:Working with Big Data in Linux - CANCELLED
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. \n \nDescription: \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.\nTHIS WORKSHOP HAS BEEN CANCELLED. It will be offered again. Apologies for any inconvenience. \nThis course does not cover specific BigData techniques such as Spark\, MapReduce\, or other large scale key-value data reduction techniques\, which will be covered in another day-long class with instructors from SDSC (TBA). \n  \nTo find out about future one-day courses\, please join our mailing list. \n>It 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 24\, 2015 \nTime: Tentatively scheduled from 9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 2011 \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-cancelled/
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2014/09/Big_Data_DARPA.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20151106T103000
DTEND;TZID=America/Los_Angeles:20151106T170000
DTSTAMP:20260508T211247
CREATED:20160504T145950Z
LAST-MODIFIED:20160504T145950Z
UID:1742-1446805800-1446829200@datascience.uci.edu
SUMMARY:Advanced R Topics: RStan & RMarkdown
DESCRIPTION:A half-day workshop on Bayesian Analysis with R-Stan\, and report generation with RMarkdown. \n \nDescription: \n \nA half-day workshop on Bayesian Analysis with R-Stan\, and report generation with RMarkdown. \nRStan is a powerful tool to do Bayesian analyses in R. While teaching students how they can do Bayesian analysis in R\, we will briefly teach RMarkdown and will compose analyses in an RMarkdown document in class. The class requires proficiency in R programming. Familiarity with Bayesian Analysis would be helpful though it’s required for this course. \nUPDATE: RShiny will be covered in the morning of this workshop. \nDate: Friday\, November 6\, 2015 \nTime:10:30am to 5 pm \nLocation: Donald Bren Hall\, Room 3011 \nInstructor: Sepehr Akhavan\, Homer Strong\, Department of Statistics\, UC Irvine \nPre-requisites:Introduction to R\, or equivalent experience. \nTo find out about future one-day courses\, please join our mailing list. \n 
URL:https://datascience.uci.edu/event/advanced-r-topics-rstan-rmarkdown/
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:20151016T090000
DTEND;TZID=America/Los_Angeles:20151016T170000
DTSTAMP:20260508T211247
CREATED:20160504T145542Z
LAST-MODIFIED:20160504T145542Z
UID:1741-1444986000-1445014800@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:October 16\, 2015 \nTime: Tentatively scheduled from 9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 3011 \nInstructor: Sepehr Akhavan\, Homer Strong\, 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. \n 
URL:https://datascience.uci.edu/event/introduction-to-r-4/
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2014/09/r-project-logo.jpg
END:VEVENT
END:VCALENDAR