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X-ORIGINAL-URL:https://datascience.uci.edu
X-WR-CALDESC:Events for Data Science Initiative
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DTSTART:20180311T100000
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DTSTART:20181104T090000
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20181026T090000
DTEND;TZID=America/Los_Angeles:20181026T170000
DTSTAMP:20260422T233416
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:20181102T090000
DTEND;TZID=America/Los_Angeles:20181102T170000
DTSTAMP:20260422T233416
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:20181109T090000
DTEND;TZID=America/Los_Angeles:20181109T170000
DTSTAMP:20260422T233416
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:20190215T090000
DTEND;TZID=America/Los_Angeles:20190215T170000
DTSTAMP:20260422T233416
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:20190222T090000
DTEND;TZID=America/Los_Angeles:20190222T170000
DTSTAMP:20260422T233416
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:20190301T090000
DTEND;TZID=America/Los_Angeles:20190301T170000
DTSTAMP:20260422T233416
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:20190308T090000
DTEND;TZID=America/Los_Angeles:20190308T170000
DTSTAMP:20260422T233416
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;VALUE=DATE:20200516
DTEND;VALUE=DATE:20200518
DTSTAMP:20260422T233416
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;VALUE=DATE:20200622
DTEND;VALUE=DATE:20200623
DTSTAMP:20260422T233416
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;TZID=UTC:20200828T100000
DTEND;TZID=UTC:20200828T170000
DTSTAMP:20260422T233416
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
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