Data Science Initiative

10/19/18 Intro to R and Data Visualization in R with ggplot
2018-10-19 09:00 - 17:00 (America/Los_Angeles) | Donald Bren Hall 4011
Intro to R:
In 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.
Data Visualization in R with ggplot:
In 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.

Neural Networks and Learning with Python
2018-05-31 09:00 - 17:00 | Donald Bren Hall 4011
This course provides an introduction to NNs and learning with Python.

In 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.
Date: May 31, 2018
Time: 9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Lingge Li, Julian Collado
TA: Koosha Azartash
Pre-requisites: 1) familiarity with basic statistical concepts, and 2) Intro-level python.
Teaching material repository:

An Introduction to Julia
2018-05-17 09:00 - 17:00 | Donald Bren Hall 4011
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.

The goal of the workshop is for students to understand where Julia can be applied and be well-equipped to start using Julia in their own research. Students will learn about the current state of Julia development (IDEs, documentation, where to get help), how to write efficient code by understanding some of Julia’s internals via small projects, solve problems using advanced Julia features (metaprogramming, multiple-dispatch, etc.), and learn workarounds to common issues newcomers face (scoping problems, type conversions, etc.).
Date: May 17, 2018
Time:9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Chris Rackauckas, UC Irvine
Pre-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.
Materials Repository:https://github.com/UCIDataScienceInitiative/IntroToJulia

SoCal Social Analytics Workshop
2018-05-11 08:00 - 17:30
Workshop for Social Media Analytics
Date: Friday, May 11, 2018
Time: 8:00 a.m. – 5:30 p.m.
Location: Calit2 Auditorium, UCI. Parking is $10 per vehicle at Anteater Parking Structure (directions)
Visit the event website for details:
https://sites.google.com/view/socalsocial2018/

Topics in R
2018-05-04 09:00 - 17:00 | Donald Bren Hall 4011
This course builds on our Introduction to R by teaching advanced visualization and data tidying.

In 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.
Date: May 4, 2018
Time:9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Dustin Pluta, UC Irvine
Pre-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.

Intro to Data Analysis with R
2018-04-20 09:00 - 17:00 | Donald Bren Hall 4011
This course provides an introduction to fundamentals of data analysis using the R language.

In 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.
Date: April 20, 2018
Time: 9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Emma Smith, Chris Galbraith
TA: Veronica Chu
Pre-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.
Teaching material repository:https://github.com/UCIDataScienceInitiative/IDA-with-R

SoCal Natural Language Processing Symposium
2018-04-06 09:00 - 17:00
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.
This 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.
Please stay tuned for more information!
Date: Friday, April 6, 2018
Time: TBD
Place: TBD
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Philosophy of Machine Learning: Knowledge and Causality
2018-03-17 08:30 - 2018-03-18 17:30 | 6011 Bren Hall, 6011 Bren Hall, UC Irvine, Irvine, CA, 92697, United States
Philosophy of Machine Learning: Knowledge and Causality
The 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.
Date: March 17-18, 2018 (Saturday-Sunday)
Time: March 17: 8:30 a.m. – 5:30 p.m.; March 18: 9:00 a.m. – 5:30 p.m.
Location: 6011 Bren Hall, UCI. Parking is $10 per vehicle per day at Anteater Parking Structure (directions)

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This 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/

Intro to Data Analysis with R
2018-03-02 09:00 - 17:00 | Donald Bren Hall 4011
This course provides an introduction to fundamentals of data analysis using the R language.

In 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.
Date: March 2, 2018
Time: 9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Emma Smith, Chris Galbraith
TA: Galen Yacalis
Pre-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.
Teaching material repository:https://github.com/UCIDataScienceInitiative/IDA-with-R

Neural Networks and Learning with Python
2018-02-23 09:00 - 17:00 | Donald Bren Hall 4011
This course provides an introduction to NNs and learning with Python.

In 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.
Date: February 23, 2018
Time: 9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Lingge Li
TA:
Pre-requisites: 1) familiarity with basic statistical concepts, and 2) Intro-level python.
Teaching material repository:

Intro to Data Analysis with R
2017-12-01 09:00 - 17:00 | Donald Bren Hall 3011
This course provides an introduction to fundamentals of data analysis using the R language.

In 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.
Date: December 1, 2017
Time: 9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 3011
Instructor: Emma Smith, Chris Galbraith
TA: Ted Grover
Pre-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.
Teaching material repository:https://github.com/UCIDataScienceInitiative/IDA-with-R

Predictive Modeling with Python
2017-11-07 09:00 - 17:00 | Donald Bren Hall 3011
Learn about the use of predictive models in Python through scikit-learn.

Python 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.
This course is targeted primarily at graduate students who have not already taken a full course in machine learning.
Date: November 7, 2017
Time: 9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 3011
Instructor: Preston Hinkle, Anna Kwa, UC Irvine
Pre-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.
Learning Materials:https://github.com/UCIDataScienceInitiative/PredictiveModeling_withPython

Intro to Data Analysis with R
2017-10-20 09:00 - 17:00
This course provides an introduction to fundamentals of data analysis using the R language.

In 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.
Date: October 20, 2017
Time: 9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Emma Smith, Chris Galbraith, UC Irvine
Pre-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.
Teaching material repository:https://github.com/UCIDataScienceInitiative/IDA-with-R

Predictive Modeling with Python
2017-10-13 09:00 - 17:00
Learn about the use of predictive models in Python through scikit-learn.

Python 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.
This course is targeted primarily at graduate students who have not already taken a full course in machine learning.
Date: October 13, 2017
Time: 9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Preston Hinkle, John Schomberg, UC Irvine
Pre-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.
Learning Materials:https://github.com/UCIDataScienceInitiative/PredictiveModeling_withPython

A Deep Introduction to Julia
2017-05-26 09:00 - 17:00
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.

The goal of the workshop is for students to understand where Julia can be applied and be well-equipped to start using Julia in their own research. Students will learn about the current state of Julia development (IDEs, documentation, where to get help), how to write efficient code by understanding some of Julia’s internals via small projects, solve problems using advanced Julia features (metaprogramming, multiple-dispatch, etc.), and learn workarounds to common issues newcomers face (scoping problems, type conversions, etc.).
Date: May 26, 2017
Time:9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Chris Rackauckas, UC Irvine
Pre-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.
Materials Repository:https://github.com/UCIDataScienceInitiative/IntroToJulia

Intro to Linux on the HPC
2017-05-16 09:00 - 17:00
This course is for researchers who have never used Linux and/or a computer cluster and introduces concepts and best practices for both.

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.
Date: May 16, 2017
Time: 9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Harry Mangalam, OIT/HPC, UC Irvine
Pre-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’.

Topics in R
2017-05-12 09:00 - 17:00
This course builds on our Introduction to R by teaching advanced visualization and data tidying.

In 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.
Date: May 12, 2017
Time:9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Colleen Nell, Dustin Pluta, UC Irvine
Pre-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.

Introduction to Spatial-Temporal Statistics
2017-04-27 09:00 - 17:00
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.

Using 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.
Date: April 27, 2017
Time:9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Gregory Britten, Yara Mohajerani, UC Irvine
Pre-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.

Predictive Modeling with Python
2017-04-21 09:00 - 17:00
Learn about the use of predictive models in Python through scikit-learn.

Python 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.
This course is targeted primarily at graduate students who have not already taken a full course in machine learning.
Date: April 21, 2016
Time:9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Preston Hinkle, John Schomberg, UC Irvine
Pre-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.
Learning Materials:https://github.com/UCIDataScienceInitiative/PredictiveModeling_withPython

Intro to R
2017-04-13 09:00 - 17:00
This course provides an introduction to the fundamentals of the R language and its applications to data analysis.

In 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.
Date:April 13, 2017
Time:9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Emma Smith, Chris Galbraith, UC Irvine
Pre-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.
Teaching material repository:https://github.com/UCIDataScienceInitiative/IntroR_Workshop

Symposium on Machine Learning and Human Behavior
2017-03-10 13:15 - 18:00
Machine Learning and Human Behavior Symposium
The last few years have seen an avalanche of new data sources providing scientists with a unique opportunity to study human behavior. In fact, much of the data now labeled as “Big Data” is generated by us, humans, as a result of our interactions with other people and our technologies. As we incessantly, tweet, email, or make online purchases, we also create new and insightful datasets.
This symposium is meant to bring together leading researchers from machine learning and the social sciences, each working at the interface between these disciplines to help us understand new opportunities and methodologies for better predicting human behavior.
Date: Friday, March 10, 2017
Time: 1:15 – 6:00 p.m.
Location: Calit2 Auditorium, UCI. Parking is $10 in the Anteater Parking Structure (directions)
This event has reached capacity. Please email academic-initiatives@uci.edu to be added to the waitlist.

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Check out Full Event Details

Time
Description

1:15 – 1:30 p.m.
Opening Remarks
Dean William Maurer, Ph.D., School of Social Sciences, University of California, Irvine

1:30 – 2:15 p.m.
Susan Athey, Ph.D., The Economics of Technology Professor, Stanford Graduate School of Business

2:15 – 3:00 p.m.
Jason Baldridge, Ph.D., Co-founder and Chief Scientist, People Pattern

3:00 – 3:15 p.m.
Coffee Break

3:15– 4:00 p.m.
Randall Lewis, Ph.D., Economic Research Scientist, Netflix

4:00– 4:45 p.m.
Yisong Yue, Ph.D., Assistant Professor of Computing and Mathematical Sciences, Caltech

4:45 – 5:00 p.m.
Closing Remarks
Padhraic Smyth, Ph.D., Director, Data Science Initiative
Professor of Computer Science and Professor of Statistics, UCI

5:00 – 6:00 p.m.
Reception

This symposium is organized by Matthew Harding, Ph.D., Associate Professor of Economics and Sameer Singh, Ph.D., Assistant Professor of Computer Science, UC Irvine

Natural Language Processing with Python
2017-03-03 09:00 - 12:00
This course introduces Natural Language Processing to users familiar with Python.

This course will introduce fundamental concepts in NLP including word and document representation, text processing, document classification, document similarity, and clustering, and dimensionality reduction.
Date: March 3, 2017
Time:9 a.m. to 12:30 p.m.
Location: Donald Bren Hall, Room 4011
Instructor: Garren Gaut, UC Irvine
Pre-requisites: 1) A previous course in programming is strongly recommended. Experience with basic machine learning is recommended.

Advanced Predictive Modeling with Python
2017-03-02 09:00 - 17:00
Learn more about the practice of predictive modeling using Python.

Description:

Learn more about the practice of predictive modeling using Python.
To find out about future one-day courses, please join our mailing list.
This 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.
This course is targeted primarily at graduate students who have not already taken a full course in machine learning.
Date: March 2, 2017
Time: 9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructors:Eric Nalisnick
Prerequisites: 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.
For course materials and more information, check out the
soon-to-be-updated Workshop page.

Topics in R
2017-02-21 09:00 - 17:00
This course builds on our Introduction to R by teaching advanced visualization and data tidying.

In 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.
Date: February 21, 2017
Time:9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Linnge Li, Dustin Pluta, UC Irvine
Pre-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.

Big Data in Brain Science Workshop
2017-02-16 13:30 - 2017-02-17 17:30
Big Data in Brain Science Workshop

The 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.
Date: February 16 -17, 2017
Time: February 16 1:30 pm – February 17 5:30 p.m.
Location: Calit2 Auditorium, UCI. Parking is $10 in the Anteater Parking Structure (directions)
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Workshop on Big Data in Brain Science
2017-02-16 13:00 - 2017-02-17 17:30
The 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.
Date and Time
February 16: 1:00 p.m. to 5:30 p.m.
February 17: 8:30 a.m. to 5:30 p.m.
Location: Calit2 Auditorium, UCI. Parking is $10 in the Anteater Parking Structure (directions)

The event is free and open to the public.
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Thursday, February 16, 2017

Time
Description

1:00 – 3:15 p.m.
Tutorial on Pre-Processing of fMRI Data
Mark Fiecas, Ph.D., Assistant Professor, Department of Biostatistics, University of Minnesota

3:15 – 3:45 p.m.
Coffee Break

3:45 – 5:30 p.m.
Tutorial on Pre-Processing of EEG Data (EEGLAB)
Scott Makeig, Ph.D., Research Scientist & Director, Swartz Center for Computational Neuroscience, University of California, San Diego

Friday, February 17, 2017

Time
Description

8:30 – 8:45 a.m.
Welcome and Introduction
Padhraic Smyth, Ph.D., Director, Data Science Initiative
Professor of Computer Science and Professor of Statistics, UCIHernando Ombao, Ph.D., Co-Organizer
Professor of Statistics and Professor of Cognitive Sciences

Session 1
Data Analytic Challenges in Neural Signals (spike train, LFP)
Moderator: Babak Shahbaba, Associate Professor of Statistics, UCI

8:45 – 9:15 a.m.
Ellen Wann
Graduate Student, Frostig Lab
Center for the Neurobiology of Learning and Memory, UCI

9:15 – 9:45 a.m.
Chi-Wing Ng
Post-doctoral Researcher, Fortin Lab
Center for the Neurobiology of Learning and Memory, UCI

9:45 – 10:15 a.m.
Sam Behseta
Professor of Mathematics and Statistics, Cal State Fullerton

10:15 – 10:45 a.m.
Discussion (led by Prof. Shahbaba)

10:45 – 11:15 a.m.
Coffee Break

Session 2
Data Analytic Challenges in fMRI
Moderator: Michele Guindani, Associate Professor of Statistics, UCI

11:15 – 11:45 a.m.
Zach Reagh
Graduate Student, Yassa Lab
Department of Neurobiology and Behavior, UCI

11:45 – 12:15 p.m.
Ron Frostig, Ph.D.
Professor of Neurobiology and Behavior, UCI

12:15 – 12:45 p.m.
Jessica Cassidy
Post-doctoral Researcher, Cramer Lab
Department of Anatomy and Neurobiology, UCI

1245 – 1:15 p.m.
Discussion (led by Prof. Guindani)

1:15 – 2:15 p.m.
Lunch Break

Session 3
Data Analytic Challenges in EEG
Moderator: Ramesh Srinivasan, Professor and Chair of Cognitive Sciences, UCI

2:15 – 2:45 p.m.
Daniel Shrey, M.D.
Pediatric Neurology, CHOC

2:45 – 3:15 p.m.
Rachel Smith
Graduate Student, Lopour Lab
Laboratory of Computational and Translational Neuroscience
Department of Biomedical Engineering, UCI

3:15 – 3:45 p.m.
Dustin Pluta
Graduate Student, Space-Time Group and Statistical Genetics
Department of Statistics, UCI

3:45 – 4:15 p.m.
Anirudh Wodeyar
Graduate Student, Srinivasan Lab
Department of Cognitive Sciences, UCI

4:15 – 4:45 p.m.
Discussion (led by Prof. Srinivasan)

Predictive Modeling with Python
2017-02-03 09:00 - 17:00
Learn about the use of predictive models in Python through scikit-learn.

Python 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.
This course is targeted primarily at graduate students who have not already taken a full course in machine learning.
Date: February 3, 2017
Time:9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Preston Hinkle, John Schomberg, Brian Vegetabile, UC Irvine
Pre-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.
Learning Materials:https://github.com/UCIDataScienceInitiative/PredictiveModeling_withPython

Intro to R
2017-01-20 09:00 - 17:00
This course provides an introduction to the fundamentals of the R language and its applications to data analysis.

In 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.
Date:January 20, 2017
Time:9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Emma Smith, Yuxiao Wang, UC Irvine
Pre-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.
Teaching material repository:https://github.com/UCIDataScienceInitiative/IntroR_Workshop

Topics in R
2016-12-02 09:00 - 17:00
This course builds on our Introduction to R by teaching advanced visualization and performance code.

In 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.
Date:Dec 2, 2016
Time:9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Linnge Li, Dustin Pluta, UC Irvine
Pre-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.

Intro to R
2016-11-18 09:00 - 17:00
This course provides an introduction to the fundamentals of the R language and its applications to data analysis.

In 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.
Date:November 18, 2016
Time:9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Emma Smith, Steven Brownlee, Yuxiao Wang, UC Irvine
Pre-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.
Teaching material repository:https://github.com/UCIDataScienceInitiative/IntroR_Workshop
