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X-WR-CALDESC:Events for Data Science Initiative
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170302T090000
DTEND;TZID=America/Los_Angeles:20170302T170000
DTSTAMP:20260420T020820
CREATED:20170227T113714Z
LAST-MODIFIED:20170227T113714Z
UID:2116-1488445200-1488474000@datascience.uci.edu
SUMMARY:Advanced Predictive Modeling with Python
DESCRIPTION:Learn more about the practice of predictive modeling using Python. \n \nDescription: \n \nLearn more about the practice of predictive modeling using Python. \nTo find out about future one-day courses\, please join our mailing list. \nThis 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: March 2\, 2017 \nTime: 9 a.m. to 5 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-python/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=image/png:https://datascience.uci.edu/wp-content/uploads/2014/09/python_2.png
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170221T090000
DTEND;TZID=America/Los_Angeles:20170221T170000
DTSTAMP:20260420T020820
CREATED:20170123T215355Z
LAST-MODIFIED:20170123T215355Z
UID:2033-1487667600-1487696400@datascience.uci.edu
SUMMARY:Topics in R
DESCRIPTION:This course builds on our Introduction to R by teaching advanced visualization and data tidying.\n \nIn this course\, you will cover advanced visualization including ggplot and R Shiny. In addition to visualizations\, principles of tidy data and reporting will be discussed. \nDate: February 21\, 2017 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Linnge Li\, Dustin Pluta\, UC Irvine \nPre-requisites: 1) familiarity with basic statistical concepts\, and 2) intermediate R programming knowledge. For the tutorial\, bring a laptop with R downloaded and installed and WiFi.
URL:https://datascience.uci.edu/event/topics-in-r-022117/
CATEGORIES:Data Science Event
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170216T133000
DTEND;TZID=America/Los_Angeles:20170217T173000
DTSTAMP:20260420T020820
CREATED:20170214T103912Z
LAST-MODIFIED:20170214T103912Z
UID:2278-1487251800-1487352600@datascience.uci.edu
SUMMARY:Big Data in Brain Science Workshop
DESCRIPTION:    \nBig Data in Brain Science Workshop\n \nThe Data Science Initiative is sponsoring a two-day workshop on February 16 and 17 highlighting recent advances on big data in brain science. The event will consist of tutorials on Feb 16 and presentations & discussions on the scientific context and data/analytic/modeling strategies on Feb 17. \nDate: February 16 -17\, 2017\nTime: February 16 1:30 pm  – February 17 5:30 p.m.\nLocation: Calit2 Auditorium\, UCI. Parking is $10 in the Anteater Parking Structure (directions) \nThis event has passed. \n \nLet people know you are going to this event.\nInvite your friends on Facebook. \nhttps://www.facebook.com/uci.datascience/videos/1781228885452303/ \nhttps://www.facebook.com/uci.datascience/videos/1781222048786320/
URL:https://datascience.uci.edu/event/big-data-brain-science-workshop/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170216T130000
DTEND;TZID=America/Los_Angeles:20170217T173000
DTSTAMP:20260420T020820
CREATED:20170111T110214Z
LAST-MODIFIED:20170111T110214Z
UID:1992-1487250000-1487352600@datascience.uci.edu
SUMMARY:Workshop on Big Data in Brain Science
DESCRIPTION: \nThe Data Science Initiative is sponsoring a two-day workshop on February 16 and 17 highlighting recent advances on big data in brain science. The event will consist of tutorials on Feb 16 and presentations & discussions on the scientific context and data/analytic/modeling strategies on Feb 17.  \nDate and Time \nFebruary 16: 1:00 p.m. to 5:30 p.m.\nFebruary 17: 8:30 a.m. to 5:30 p.m. \nLocation: Calit2 Auditorium\, UCI. Parking is $10 in the Anteater Parking Structure (directions) \n \nThe event is free and open to the public.\nInvite your friends on Facebook.  \nThursday\, February 16\, 2017 \n\n\n\n\nTime                   \nDescription\n\n\n\n\n1:00 – 3:15 p.m.\nTutorial on Pre-Processing of fMRI Data\nMark Fiecas\, Ph.D.\, Assistant Professor\, Department of Biostatistics\, University of Minnesota\n\n\n3:15 – 3:45 p.m.   \nCoffee Break\n\n\n3:45 – 5:30 p.m.\nTutorial on Pre-Processing of EEG Data (EEGLAB)\nScott Makeig\, Ph.D.\, Research Scientist & Director\, Swartz Center for Computational Neuroscience\, University of California\, San Diego\n\n\n\n\nFriday\, February 17\, 2017\n\n\n\n\nTime                   \nDescription\n\n\n\n\n8:30 – 8:45 a.m.\nWelcome and Introduction\nPadhraic Smyth\, Ph.D.\, Director\, Data Science Initiative\nProfessor of Computer Science and Professor of Statistics\, UCIHernando Ombao\, Ph.D.\, Co-Organizer\nProfessor of Statistics and Professor of Cognitive Sciences\n\n\nSession 1\nData Analytic Challenges in Neural Signals (spike train\, LFP)\nModerator: Babak Shahbaba\, Associate Professor of Statistics\, UCI\n\n\n8:45 – 9:15 a.m.\nEllen Wann\nGraduate Student\, Frostig Lab\nCenter for the Neurobiology of Learning and Memory\, UCI\n\n\n9:15 – 9:45 a.m.   \nChi-Wing Ng\nPost-doctoral Researcher\, Fortin Lab\nCenter for the Neurobiology of Learning and Memory\, UCI\n\n\n9:45 – 10:15 a.m.\nSam Behseta\nProfessor of Mathematics and Statistics\, Cal State Fullerton \n\n\n10:15 – 10:45 a.m.   \nDiscussion (led by Prof. Shahbaba)\n\n\n10:45 – 11:15 a.m.  \nCoffee Break\n\n\nSession 2\nData Analytic Challenges in fMRI\n Moderator: Michele Guindani\, Associate Professor of Statistics\, UCI\n\n\n11:15 – 11:45 a.m.   \nZach Reagh\nGraduate Student\, Yassa Lab\nDepartment of Neurobiology and Behavior\, UCI\n\n\n11:45 – 12:15 p.m.\nRon Frostig\, Ph.D.\nProfessor of Neurobiology and Behavior\, UCI\n\n\n12:15 – 12:45 p.m.   \nJessica Cassidy\nPost-doctoral Researcher\, Cramer Lab\nDepartment of Anatomy and Neurobiology\, UCI\n\n\n1245 – 1:15 p.m.\nDiscussion (led by Prof. Guindani)\n\n\n1:15 – 2:15 p.m.   \nLunch Break\n\n\nSession 3\nData Analytic Challenges in EEG\n Moderator: Ramesh Srinivasan\, Professor and Chair of Cognitive Sciences\, UCI\n\n\n2:15 – 2:45 p.m.   \nDaniel Shrey\, M.D.\nPediatric Neurology\, CHOC\n\n\n2:45 – 3:15 p.m.\nRachel Smith\nGraduate Student\, Lopour Lab\nLaboratory of Computational and Translational Neuroscience\nDepartment of Biomedical Engineering\, UCI \n\n\n3:15 – 3:45 p.m.   \nDustin Pluta\nGraduate Student\, Space-Time Group and Statistical Genetics\nDepartment of Statistics\, UCI\n\n\n3:45 – 4:15 p.m.\nAnirudh Wodeyar\nGraduate Student\, Srinivasan Lab\nDepartment of Cognitive Sciences\, UCI\n\n\n4:15 – 4:45 p.m.   \nDiscussion (led by Prof. Srinivasan)
URL:https://datascience.uci.edu/event/workshop-on-big-data-in-brain-science/
CATEGORIES:Data Science Event
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170203T090000
DTEND;TZID=America/Los_Angeles:20170203T170000
DTSTAMP:20260420T020820
CREATED:20170117T111019Z
LAST-MODIFIED:20170117T111019Z
UID:2019-1486112400-1486141200@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: February 3\, 2017 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Preston Hinkle\, John Schomberg\, Brian Vegetabile\, 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-python-020317/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170120T090000
DTEND;TZID=America/Los_Angeles:20170120T170000
DTSTAMP:20260420T020820
CREATED:20170112T195308Z
LAST-MODIFIED:20170112T195308Z
UID:2017-1484902800-1484931600@datascience.uci.edu
SUMMARY:Intro 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:January 20\, 2017 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Emma Smith\, Yuxiao Wang\, 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/intro-to-r/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20161202T090000
DTEND;TZID=America/Los_Angeles:20161202T170000
DTSTAMP:20260420T020820
CREATED:20161121T100342Z
LAST-MODIFIED:20161121T100342Z
UID:1980-1480669200-1480698000@datascience.uci.edu
SUMMARY:Topics in R
DESCRIPTION:This course builds on our Introduction to R by teaching advanced visualization and performance code.\n \nIn this course\, you will cover advanced visualization including ggplot and R Shiny. In addition to visualizations\, the latter half of the course will include methods for speeding up R code. If time permits\, data wrangling and version control will be included. Practical examples will be provided during the course. \nDate:Dec 2\, 2016 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Linnge Li\, Dustin Pluta\, UC Irvine \nPre-requisites: 1) familiarity with basic statistical concepts\, and 2) intermediate R programming knowledge. For the tutorial\, bring a laptop with R downloaded and installed and WiFi.
URL:https://datascience.uci.edu/event/topics-in-r/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20161118T090000
DTEND;TZID=America/Los_Angeles:20161118T170000
DTSTAMP:20260420T020820
CREATED:20161008T170815Z
LAST-MODIFIED:20161008T170815Z
UID:1909-1479459600-1479488400@datascience.uci.edu
SUMMARY:Intro 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:November 18\, 2016 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Emma Smith\, Steven Brownlee\, Yuxiao Wang\, 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/intro-r-111816/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20161107T090000
DTEND;TZID=America/Los_Angeles:20161107T170000
DTSTAMP:20260420T020820
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
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20161025T090000
DTEND;TZID=America/Los_Angeles:20161025T170000
DTSTAMP:20260420T020820
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
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20161020T090000
DTEND;TZID=America/Los_Angeles:20161020T170000
DTSTAMP:20260420T020820
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
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20161014T160000
DTEND;TZID=America/Los_Angeles:20161014T170000
DTSTAMP:20260420T020820
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=:
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20161007T150000
DTEND;TZID=America/Los_Angeles:20161007T183000
DTSTAMP:20260420T020820
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=:
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20161007T090000
DTEND;TZID=America/Los_Angeles:20161007T170000
DTSTAMP:20260420T020820
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:20260420T020820
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
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160531T090000
DTEND;TZID=America/Los_Angeles:20160531T170000
DTSTAMP:20260420T020820
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=:
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160520T090000
DTEND;TZID=America/Los_Angeles:20160520T200000
DTSTAMP:20260420T020820
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=:
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160514T140000
DTEND;TZID=America/Los_Angeles:20160515T200000
DTSTAMP:20260420T020820
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=:
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160506T120000
DTEND;TZID=America/Los_Angeles:20160506T170000
DTSTAMP:20260420T020820
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=:
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160422T090000
DTEND;TZID=America/Los_Angeles:20160422T170000
DTSTAMP:20260420T020820
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=:
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