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DTSTART;TZID=UTC:20200828T100000
DTEND;TZID=UTC:20200828T170000
DTSTAMP:20260422T215851
CREATED:20200811T211932Z
LAST-MODIFIED:20200811T212511Z
UID:2740-1598608800-1598634000@datascience.uci.edu
SUMMARY:Experimental Design - The Key to Reliable & Reproducible Science
DESCRIPTION:Who should attend? \nAnyone interested in designing\, conducting\, or analyzing (controlled) experiments to answer scientific questions involving group comparisons. \nDescription: \nObtaining reliable and reproducible estimates of a “treatment” effect and drawing conclusions in any scientific context hinges on sound study design. No statistical analysis can salvage a poorly designed study. Before rushing to pick the “ideal” statistical analysis\, we must think about the scientific objective and aims and ask: what is the scientific question of interest? \nThen we will examine how and why the design of a study can greatly impact the reliability of results and the conclusions we can draw from any data analysis. How we design a study and collect data will determine the generalizability of results\, including whether reproducing findings in future studies would be a reasonable endeavor. To this end\, the overall goal of this short course is two-fold: (1) to introduce and motivate experimental design\, and (2) to explore the utility and appropriateness of different types of experimental designs\, including clinical trials\, to address a given scientific question of interest. \nThis short course is geared towards individuals who are interested in designing\, conducting\, or analyzing (controlled) experiments to answer scientific questions involving group comparisons (e.g.\, difference in means between two groups). While familiarity with analytic approaches to compare means between two groups (t-test)\, more than two groups (analysis of variance\, ANOVA)\, and many more than two groups (linear regression) can be helpful\, the intention of this course is not to delve into statistical theory. Instead\, the focus will be to explain core concepts of study design\, highlighting the scientific and statistical considerations that should be discussed among all collaborators (including the statistician)\, ideally at the earliest stage of a project before data has been collected. \nThis short course will be divided into three sessions:\n10AM-Noon\, 1PM-3PM\, 4PM-5PM. \n\n\n\n\n\n\n\n\n\nRegister Online Now!\n\n\n\n\n\n\n\n\n\n\n\nInstructor Bio: \n\n\n\n\n\n\n\n\n\nNavneet Hakhu is a second year Statistics PhD student at UCI working alongside advisors Drs. Daniel Gillen (Statistics) and Joshua Grill (Psychiatry & Human Behavior; Neurobiology & Behavior) on statistical methods with applications to Alzheimer’s disease research. Prior to enrolling in the UCI Statistics program\, Navneet worked as an associate specialist at UCI MIND (Institute for Memory Impairments and Neurological Disorders) with Drs. Gillen and Grill. Before rejoining academia in 2018\, Navneet served primarily as an independent statistician at Axio Research in Seattle\, Washington for over 3.5 years where he supported 29 phase 2 to 4 clinical trials and participated in 74 Data Monitoring Committee meetings. Navneet holds an MS degree in Biostatistics from the University of Washington.
URL:https://datascience.uci.edu/event/experimental-design-the-key-to-reliable-reproducible-science/
LOCATION:Zoom\, *Link will be emailed to registrants
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2020/08/Experimental_Design_Workshop_Flyer_20200806-7ccf5702dde09f46.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20200622
DTEND;VALUE=DATE:20200623
DTSTAMP:20260422T215851
CREATED:20200814T162137Z
LAST-MODIFIED:20200814T162137Z
UID:2761-1592784000-1592870399@datascience.uci.edu
SUMMARY:Data Exploration and Visualization using R and ggplot2
DESCRIPTION:About this Event\n\n\n*Time:* June 22 (Monday) 10AM-12PM\, 1PM-3PM\, 4PM-5PM (Participants are registered for all three sessions.) \n*Name:* Data Exploration and Visualization using R and ggplot2 \n(Zoom meeting link will be emailed to registered attendees two days prior to the event.) \n*Description:* This course is intended to help people with basic experience using R take their first steps on a data analysis. We provide a quick overview of introductory R programming\, then focus on using R and ggplot2 for data exploration and visualization. Note: planned topics for each session may change as time constraints dictate. \n*Syllabus:* \n*10AM-12PM session:* \n* R and RStudio basics: data types\, subsetting\, reading/writing data\, loops and functions. \n* Basic data exploration and plotting in base R: summaries\, tables\, plotting different types of data. \n*1PM-3PM Session:* \n* Introduction to ggplot2 and the “grammar of graphics:” understanding the logic of ggplot2\, preparing and plotting data. \n* Beyond ggplot2 basics: overlaying geoms\, visualizing strata for comparison\, changing default plot options. \n*4PM-5PM session:* \n* Example: making a choropleth map in ggplot2. \n****FOR SPECIFIC COURSE CONTENT QUESTIONS\, PLEASE CONTACT COURSE INSTRUCTOR ARNIE SEONG: akseong@uci.edu *****
URL:https://datascience.uci.edu/event/data-exploration-and-visualization-using-r-and-ggplot2/
LOCATION:Zoom
CATEGORIES:Data Science Event
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20200516
DTEND;VALUE=DATE:20200518
DTSTAMP:20260422T215851
CREATED:20200814T160504Z
LAST-MODIFIED:20200814T160620Z
UID:2754-1589587200-1589759999@datascience.uci.edu
SUMMARY:Introduction to Data Analysis with R
DESCRIPTION:Description\nThis course provides a brief introduction to the fundamentals of the R language and focuses on its use for data analysis–including exploratory data analysis\, linear and logistic regression\, variable selection\, model diagnostics\, and prediction. \nSyllabus \n\nFundamentals of R & RStudio: the basics–including objects\, subsetting\, indexing\, data I/O\, and control structures\nExploratory Data Analysis: all the necessary tools to investigate your data before performing any formal modeling–from summary statistics to visualization including plotting histograms\, boxplots\, and scatterplots\nLinear Regression: everything you need to know to begin fitting linear models–from simple t-tests to estimation of regression coefficients\, variable selection\, and prediction\nLogistic Regression: the basics of generalized linear models (GLMs) with an emphasis on binary response data–we extend the theory and modeling strategies of linear regression
URL:https://datascience.uci.edu/event/introduction-to-data-analysis-with-r/
LOCATION:Zoom
CATEGORIES:Data Science Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190308T090000
DTEND;TZID=America/Los_Angeles:20190308T170000
DTSTAMP:20260422T215851
CREATED:20190304T155414Z
LAST-MODIFIED:20190304T155414Z
UID:2409-1552035600-1552064400@datascience.uci.edu
SUMMARY:03/08/19 Intro to Deep Generative Models
DESCRIPTION:This workshop aims at introducing commonly used deep neural networks and their application as deep generative models. We will cover motivating ideas and theory behind various deep generative models such as variational auto-encoder (VAE)\, generative adversarial network (GAN) and flow-based model. You will learn to implement these models to generate realistic looking images in Tensorflow.
URL:https://datascience.uci.edu/event/03-08-19-intro-deep-generative-models/
LOCATION:Donald Bren Hall 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190301T090000
DTEND;TZID=America/Los_Angeles:20190301T170000
DTSTAMP:20260422T215851
CREATED:20190226T103653Z
LAST-MODIFIED:20190226T103653Z
UID:2408-1551430800-1551459600@datascience.uci.edu
SUMMARY:03/01/19 A Review of Graph Convolutional Neural Networks
DESCRIPTION:The first part of this workshop will be a review of neural networks in tensorflow and keras. The second part will go into an exciting specific type of neural network called graph convolutional neural networks. There are numerous real-world data in non-euclidean relations. Finding an optimum representation of these types of data can be useful to investigate their hidden patterns and structures. 2-d manifolds in a 3-d space and graph-embedded relations are two important examples of data points in a non-euclidean relation. Graph convolutional neural networks\, as an emerging and surprisingly successful tool\, can be used to capture these relations. We cover the mathematics behind this method and provide a survey on the most recent works on GCNs. We also dive into the implementation of one or two basic networks of these types.
URL:https://datascience.uci.edu/event/03-01-19-review-graph-convolutional-neural-networks/
LOCATION:Donald Bren Hall 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190222T090000
DTEND;TZID=America/Los_Angeles:20190222T170000
DTSTAMP:20260422T215851
CREATED:20190214T191105Z
LAST-MODIFIED:20190214T191105Z
UID:2374-1550826000-1550854800@datascience.uci.edu
SUMMARY:02/22/19 Intro to Linux
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.
URL:https://datascience.uci.edu/event/02-22-19-intro-linux/
LOCATION:Donald Bren Hall 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190215T090000
DTEND;TZID=America/Los_Angeles:20190215T170000
DTSTAMP:20260422T215851
CREATED:20190205T180902Z
LAST-MODIFIED:20190205T180902Z
UID:2407-1550221200-1550250000@datascience.uci.edu
SUMMARY:02/15/19 Introduction to Deep Learning
DESCRIPTION:In this workshop\, you’ll learn basic ideas of neural networks and Tensorflow programming fundamentals through building and training different models. Moreover\, you will be introduced to more advanced applications of deep learning in computer vision and natural language processing with Keras high-level API.
URL:https://datascience.uci.edu/event/02-15-19-introduction-deep-learning/
LOCATION:Donald Bren Hall 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20181109T090000
DTEND;TZID=America/Los_Angeles:20181109T170000
DTSTAMP:20260422T215851
CREATED:20181102T140415Z
LAST-MODIFIED:20181102T140415Z
UID:2406-1541754000-1541782800@datascience.uci.edu
SUMMARY:11/09/18 An 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 v1.0 language which can help produce efficient and readable code. \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.).
URL:https://datascience.uci.edu/event/11-09-18-introduction-julia/
LOCATION:Donald Bren Hall 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20181102T090000
DTEND;TZID=America/Los_Angeles:20181102T170000
DTSTAMP:20260422T215851
CREATED:20181030T113436Z
LAST-MODIFIED:20181030T113436Z
UID:2405-1541149200-1541178000@datascience.uci.edu
SUMMARY:11/02/18 Topics in R
DESCRIPTION:This course builds on our Introduction to R by teaching advanced visualization and data tidying. \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. \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/11-02-18-topics-r/
LOCATION:Donald Bren Hall 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20181026T090000
DTEND;TZID=America/Los_Angeles:20181026T170000
DTSTAMP:20260422T215851
CREATED:20181017T094207Z
LAST-MODIFIED:20181017T094207Z
UID:2404-1540544400-1540573200@datascience.uci.edu
SUMMARY:10/26/18 Intro to Linux on the HPC
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. \nDate: Friday\, October 26\, 2018\nTime: 9am-5pm (coffee and lunch will be provided)\nLocation: Donald Bren Hall\, Room 4011\nCost:\nRegister online before 10/26/18: $10\nWalk-ins: $15 (cash or check payable to “UC Regents” only)
URL:https://datascience.uci.edu/event/10-26-18-intro-linux-hpc/
LOCATION:Donald Bren Hall 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20181019T090000
DTEND;TZID=America/Los_Angeles:20181019T170000
DTSTAMP:20260422T215851
CREATED:20181010T092916Z
LAST-MODIFIED:20181010T092916Z
UID:2403-1539939600-1539968400@datascience.uci.edu
SUMMARY:10/19/18 Intro to R and Data Visualization in R with ggplot
DESCRIPTION:Intro to R:\nIn this session\, students will be familiarized with R: data types\, functions and basic data manipulation including some exploratory data analysis and how to perform statistical tests. \nData Visualization in R with ggplot:\nIn this part of the workshop\, students will learn the basic commands to create statistical plots\, understand the grammar of graphics behind ggplot\, and master how to create more sophisticated data visualizations through hands-on exercises on real data sets.
URL:https://datascience.uci.edu/event/10-19-18-intro-r-data-visualization-r-ggplot/
LOCATION:Donald Bren Hall 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180531T090000
DTEND;TZID=America/Los_Angeles:20180531T170000
DTSTAMP:20260422T215851
CREATED:20180402T104709Z
LAST-MODIFIED:20180402T104709Z
UID:2401-1527757200-1527786000@datascience.uci.edu
SUMMARY:Neural Networks and Learning with Python
DESCRIPTION:This course provides an introduction to NNs and learning with Python.\n \nIn this workshop\, you will learn about Tensorflow programming fundamentals and basic ideas of neural networks through building and training different MNIST models. Moreover\, you will be introduced to more advanced applications of deep learning in computer vision and natural language processing with Keras high-level API. \nDate: May 31\, 2018 \nTime: 9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor:  Lingge Li\, Julian Collado \nTA:  Koosha Azartash \nPre-requisites: 1) familiarity with basic statistical concepts\, and 2) Intro-level python. \nTeaching material repository:
URL:https://datascience.uci.edu/event/neural-networks-learning-python/
LOCATION:Donald Bren Hall 4011
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:20180517T090000
DTEND;TZID=America/Los_Angeles:20180517T170000
DTSTAMP:20260422T215851
CREATED:20180402T105400Z
LAST-MODIFIED:20180402T105400Z
UID:2402-1526547600-1526576400@datascience.uci.edu
SUMMARY:An 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: May 17\, 2018 \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/an-introduction-to-julia/
LOCATION:Donald Bren Hall 4011
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180511T080000
DTEND;TZID=America/Los_Angeles:20180511T173000
DTSTAMP:20260422T215851
CREATED:20180205T123435Z
LAST-MODIFIED:20180205T123435Z
UID:2398-1526025600-1526059800@datascience.uci.edu
SUMMARY:SoCal Social Analytics Workshop
DESCRIPTION:Workshop for Social Media Analytics\nDate: Friday\, May 11\, 2018\nTime: 8:00 a.m. – 5:30 p.m.\nLocation: Calit2 Auditorium\, UCI. Parking is $10 per vehicle at Anteater Parking Structure (directions) \nVisit the event website for details: \nhttps://sites.google.com/view/socalsocial2018/ \n  \n 
URL:https://datascience.uci.edu/event/social_analytics/
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180504T090000
DTEND;TZID=America/Los_Angeles:20180504T170000
DTSTAMP:20260422T215851
CREATED:20180402T105609Z
LAST-MODIFIED:20180402T105609Z
UID:2357-1525424400-1525453200@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: May 4\, 2018 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor:  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-3/
LOCATION:Donald Bren Hall 4011
CATEGORIES:Data Science Event
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:20180420T090000
DTEND;TZID=America/Los_Angeles:20180420T170000
DTSTAMP:20260422T215851
CREATED:20180402T103605Z
LAST-MODIFIED:20180402T103605Z
UID:2400-1524214800-1524243600@datascience.uci.edu
SUMMARY:Intro to Data Analysis with R
DESCRIPTION:This course provides an introduction to fundamentals of data analysis using the R language.\n \nIn this course\, you will learn how to effectively use R for data analysis. This course provides a brief introduction to the fundamentals of the R language and focuses on its use for data analysis–including exploratory data analysis\, linear and logistic regression\, variable selection\, model diagnostics\, and prediction. Practical examples will be provided during the course. \nDate: April 20\, 2018 \nTime: 9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Emma Smith\, Chris Galbraith \nTA:  Veronica Chu \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/IDA-with-R
URL:https://datascience.uci.edu/event/intro-data-analysis-r-4/
LOCATION:Donald Bren Hall 4011
CATEGORIES:Data Science Event
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:20180406T090000
DTEND;TZID=America/Los_Angeles:20180406T170000
DTSTAMP:20260422T215851
CREATED:20180312T135822Z
LAST-MODIFIED:20180312T135822Z
UID:2399-1523005200-1523034000@datascience.uci.edu
SUMMARY:SoCal Natural Language Processing Symposium
DESCRIPTION:The goal of Southern California Natural Language Processing Symposium is to gather researchers from the southern California region with broad expertise in natural language processing. The symposium will provide a participatory environment where attendees from a variety of fields working on natural language processing can share and discuss their latest findings. \nThis year\, SoCal NLP Symposium will be hosted on April 6\, 2018 at University of California\, Irvine\, and it will include invited talks from academia and industry\, contributed work\, poster presentations and open discussion. We welcome all students\, postdocs\, and faculty members from universities in the region\, including UC Irvine\, University of Southern California (USC)\, UC Los Angeles\, UC Santa Barbara\, UC Riverside\, Caltech\, UC San Diego\, and other schools to join us this April. \nPlease stay tuned for more information! \nDate: Friday\, April 6\, 2018 \nTime: TBD \nPlace: TBD \n Invite your friends on Facebook!
URL:https://datascience.uci.edu/event/socal-natural-language-processing-symposium/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180317T083000
DTEND;TZID=America/Los_Angeles:20180318T173000
DTSTAMP:20260422T215851
CREATED:20170920T095927Z
LAST-MODIFIED:20170920T095927Z
UID:2246-1521275400-1521394200@datascience.uci.edu
SUMMARY:Philosophy of Machine Learning: Knowledge and Causality
DESCRIPTION:Philosophy of Machine Learning: Knowledge and Causality\nThe recent rapid development in machine learning has opened up unprecedented possibilities in analyzing and predicting diverse phenomena. From the humanities\, to the social and cognitive sciences\, to the natural sciences\, fields previously closed off to predictive analysis are now the subject of machine learning investigations. The purpose is to introduce workshop participants to a diverse collection of perspectives and methodologies in the hope of engendering further interdisciplinary thinking. \nDate:  March 17-18\, 2018 (Saturday-Sunday)\nTime: March 17: 8:30 a.m. – 5:30 p.m.; March 18: 9:00 a.m. – 5:30 p.m.\nLocation:  6011 Bren Hall\, UCI. Parking is $10 per vehicle per day at Anteater Parking Structure (directions) \n \n Invite your Facebook friends! \nThis workshop is made possible by the generous support provided by the School of Social Sciences\, the Department of Logic and Philosophy of Science\, and the Data Science initiative\, all at UC Irvine. For more information about this workshop\, visit: https://philmachinelearning.wordpress.com/
URL:https://datascience.uci.edu/event/philosophy-machine-learning-knowledge-causality/
LOCATION:6011 Bren Hall\, 6011 Bren Hall\, UC Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180302T090000
DTEND;TZID=America/Los_Angeles:20180302T170000
DTSTAMP:20260422T215851
CREATED:20180116T081358Z
LAST-MODIFIED:20180116T081358Z
UID:2317-1519981200-1520010000@datascience.uci.edu
SUMMARY:Intro to Data Analysis with R
DESCRIPTION:This course provides an introduction to fundamentals of data analysis using the R language.\n \nIn this course\, you will learn how to effectively use R for data analysis. This course provides a brief introduction to the fundamentals of the R language and focuses on its use for data analysis–including exploratory data analysis\, linear and logistic regression\, variable selection\, model diagnostics\, and prediction. Practical examples will be provided during the course. \nDate: March 2\, 2018 \nTime: 9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Emma Smith\, Chris Galbraith \nTA:  Galen Yacalis \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/IDA-with-R
URL:https://datascience.uci.edu/event/intro-data-analysis-r-3/
LOCATION:Donald Bren Hall 4011
CATEGORIES:Data Science Event
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:20180223T090000
DTEND;TZID=America/Los_Angeles:20180223T170000
DTSTAMP:20260422T215851
CREATED:20180116T081909Z
LAST-MODIFIED:20180116T081909Z
UID:2321-1519376400-1519405200@datascience.uci.edu
SUMMARY:Neural Networks and Learning with Python
DESCRIPTION:This course provides an introduction to NNs and learning with Python.\n \nIn this workshop\, you will learn about Tensorflow programming fundamentals and basic ideas of neural networks through building and training different MNIST models. Moreover\, you will be introduced to more advanced applications of deep learning in computer vision and natural language processing with Keras high-level API. \nDate: February 23\, 2018 \nTime: 9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor:  Lingge Li \nTA:  \nPre-requisites: 1) familiarity with basic statistical concepts\, and 2) Intro-level python. \nTeaching material repository:
URL:https://datascience.uci.edu/event/machine-learning-python/
LOCATION:Donald Bren Hall 4011
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:20171201T090000
DTEND;TZID=America/Los_Angeles:20171201T170000
DTSTAMP:20260422T215851
CREATED:20170928T152340Z
LAST-MODIFIED:20170928T152340Z
UID:2265-1512118800-1512147600@datascience.uci.edu
SUMMARY:Intro to Data Analysis with R
DESCRIPTION:This course provides an introduction to fundamentals of data analysis using the R language.\n \nIn this course\, you will learn how to effectively use R for data analysis. This course provides a brief introduction to the fundamentals of the R language and focuses on its use for data analysis–including exploratory data analysis\, linear and logistic regression\, variable selection\, model diagnostics\, and prediction. Practical examples will be provided during the course. \nDate: December 1\, 2017 \nTime: 9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 3011 \nInstructor: Emma Smith\, Chris Galbraith \nTA:  Ted Grover \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/IDA-with-R
URL:https://datascience.uci.edu/event/intro-data-analysis-r-2/
LOCATION:Donald Bren Hall 3011
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:20171107T090000
DTEND;TZID=America/Los_Angeles:20171107T170000
DTSTAMP:20260422T215851
CREATED:20170928T150356Z
LAST-MODIFIED:20170928T150356Z
UID:2260-1510045200-1510074000@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\, 2017 \nTime: 9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 3011 \nInstructor: Preston Hinkle\, Anna Kwa\, 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-3/
LOCATION:Donald Bren Hall 3011
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:20171020T090000
DTEND;TZID=America/Los_Angeles:20171020T170000
DTSTAMP:20260422T215851
CREATED:20170928T151625Z
LAST-MODIFIED:20170928T151625Z
UID:2262-1508490000-1508518800@datascience.uci.edu
SUMMARY:Intro to Data Analysis with R
DESCRIPTION:This course provides an introduction to fundamentals of data analysis using the R language.\n \nIn this course\, you will learn how to effectively use R for data analysis. This course provides a brief introduction to the fundamentals of the R language and focuses on its use for data analysis–including exploratory data analysis\, linear and logistic regression\, variable selection\, model diagnostics\, and prediction. Practical examples will be provided during the course. \nDate: October 20\, 2017 \nTime: 9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Emma Smith\, Chris Galbraith\, 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/IDA-with-R
URL:https://datascience.uci.edu/event/intro-data-analysis-r/
CATEGORIES:Data Science Event
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:20171013T090000
DTEND;TZID=America/Los_Angeles:20171013T170000
DTSTAMP:20260422T215851
CREATED:20170928T142509Z
LAST-MODIFIED:20170928T142509Z
UID:2257-1507885200-1507914000@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: October 13\, 2017 \nTime: 9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Preston Hinkle\, John Schomberg\, 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-2/
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:20170526T090000
DTEND;TZID=America/Los_Angeles:20170526T170000
DTSTAMP:20260422T215851
CREATED:20170407T121417Z
LAST-MODIFIED:20170407T121417Z
UID:2161-1495789200-1495818000@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: May 26\, 2017 \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/deep-introduction-julia/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170516T090000
DTEND;TZID=America/Los_Angeles:20170516T170000
DTSTAMP:20260422T215851
CREATED:20170407T120926Z
LAST-MODIFIED:20170407T120926Z
UID:2159-1494925200-1494954000@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: May 16\, 2017\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’. \n 
URL:https://datascience.uci.edu/event/intro-linux-hpc/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2015/07/16079640189_9733eba311_z.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170512T090000
DTEND;TZID=America/Los_Angeles:20170512T170000
DTSTAMP:20260422T215851
CREATED:20170407T120351Z
LAST-MODIFIED:20170407T120351Z
UID:2157-1494579600-1494608400@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: May 12\, 2017 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor:  Colleen Nell\, 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-2/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170427T090000
DTEND;TZID=America/Los_Angeles:20170427T170000
DTSTAMP:20260422T215851
CREATED:20170414T104243Z
LAST-MODIFIED:20170414T104243Z
UID:2166-1493283600-1493312400@datascience.uci.edu
SUMMARY:Introduction to Spatial-Temporal Statistics
DESCRIPTION:Data collected in time and/or space exhibit unique properties that require attention to draw proper conclusions from statistical analyses. In this workshop\, students are introduced to statistical concepts that are particularly useful for analyzing spatial-temporal data.\n \nUsing R and Python\, students will learn the basic mathematics of spatial-temporal analysis via hands-on exercises\, and will put the concepts to practice analyzing real datasets from the environmental sciences. Key R packages will be used in the workshop\, while Python functionality to call R from within Python will also be provided\, therefore accommodating both R and Python users. An introductory knowledge of either R or Python is therefore required to take part in the exercises. A background with either one statistics course or some experience analyzing spatial and/or temporal data is also recommended. While the datasets are drawn from the environmental sciences\, the concepts apply equally across disciplines where data are collected in time and/or space. \nDate: April 27\, 2017 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor:  Gregory Britten\, Yara Mohajerani\, UC Irvine \nPre-requisites: 1) familiarity with basic statistical concepts\, and 2) intermediate R or Python programming knowledge. For the tutorial\, bring a laptop with R or Python downloaded and installed and WiFi.
URL:https://datascience.uci.edu/event/2166/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170421T090000
DTEND;TZID=America/Los_Angeles:20170421T170000
DTSTAMP:20260422T215851
CREATED:20170407T114244Z
LAST-MODIFIED:20170407T114244Z
UID:2154-1492765200-1492794000@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: April 21\, 2016 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Preston Hinkle\, John Schomberg\, 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/
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:20170413T090000
DTEND;TZID=America/Los_Angeles:20170413T170000
DTSTAMP:20260422T215851
CREATED:20170407T114306Z
LAST-MODIFIED:20170407T114306Z
UID:2152-1492074000-1492102800@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:April 13\, 2017 \nTime:9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Emma Smith\, Chris Galbraith\, 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-2/
CATEGORIES:Data Science Event
ATTACH;FMTTYPE=:
END:VEVENT
END:VCALENDAR