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X-WR-CALDESC:Events for Data Science Initiative
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DTSTART;TZID=America/Los_Angeles:20170413T090000
DTEND;TZID=America/Los_Angeles:20170413T170000
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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=:
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DTSTART;TZID=America/Los_Angeles:20170421T090000
DTEND;TZID=America/Los_Angeles:20170421T170000
DTSTAMP:20260629T232236
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
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DTSTART;TZID=America/Los_Angeles:20170427T090000
DTEND;TZID=America/Los_Angeles:20170427T170000
DTSTAMP:20260629T232236
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
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