Data Science Initiative

Intro to Data Analysis with R
2017-12-01 09:00 - 17:00 (America/Los_Angeles) | 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

Predictive Modeling with Python
2016-11-07 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:November 7, 2016
Time:9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Brian Vegetabile, Eric Nalisnick, Christine Lee, 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
2016-10-25 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:October 25, 2016
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

Next Generation Sequencing Data Analysis
2016-10-20 09:00 - 17:00
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.

In 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.
Date:October 20, 2016
Time:9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Jenny Wu, UC Irvine
Pre-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).
Learning Materials:http://ghtf.biochem.uci.edu/content/ngs-data-analysis

Information Session on the Insight Data Science Fellows Program
2016-10-14 16:00 - 17:00
Information Session on the Insight Data Science Fellows Program
Speaker: Emily Thompson, Insight Data Science, insightdatascience.com
Target audience: Graduate students and Postdoctoral researchers
Date: Friday, October 14
Time: 4:00 to 5:00 p.m.
Location: Calit2 Auditorium, UCI (directions)
RSVP Today
About Emily Thompson
Emily 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.

About Insight Fellows Program
The 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.
In 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

Symposium on Recent Advances in Data Science
2016-10-07 15:00 - 18:30
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.
Date: October 7, 2016
Time: 3:00 – 6:30 p.m.
Location: Calit2 Auditorium (directions)
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Schedule:

Time
Description

3:00 p.m.
Update on data science at UCI
Padhraic Smyth, Director, UCI Data Science Initiative

3:20 p.m.
Research Highlight: Interpreting machine learning predictions
Sameer Singh, Assistant Professor, Department of Computer Science, UCI

3:50 p.m.
Research Highlight: A Bayesian perspective on publication bias in the Reproducibility Project: Psychology
Joachim Vandekerckhove, Associate Professor, Department of Cognitive Sciences, UCI

4:20 p.m.
Invited Keynote: Google data and public sentiment
Dr. Steve Scott, Senior Economic Analyst, Google

5:00 p.m.
Reception and graduate student poster session

[/two_third]

Submit a Poster
UCI 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.

Deadline has passed

Introduction to R
2016-10-07 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:October 7, 2016
Time:9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 4011
Instructor: Emma Smith, Steven Brownlee, 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

Intro to Linux on the HPC
2016-10-03 09:00 - 17:00 | Donald Bren Hall 3011
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:October 3, 2016
Time: 9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 3011
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’.

Introduction to R
2016-05-31 09:00 - 17:00
This course provides an introduction to the fundamentals of the R language and its applications to data analysis.
Date:May 31, 2016
Time:9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 3011

In 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.
Date:May 31, 2016
Time:9 a.m. to 5 p.m. with lunch provided
Location: Donald Bren Hall, Room 3011
Instructor: Emma Smith, Wen He, 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

Introduction to Julia
2016-05-24 09:00 - 13:00
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.

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.
To find out about future one-day courses, please join our mailing list.
Julia is a relatively new language which has been making waves in the scientific community due to its ease of use as a scripting language combined with its ability to produce programs with comparable runtimes to C/Fortran. For this reason, Julia is situated is fast becoming a major language in high-performance and “big data” computing. However, the current state of the Julia ecosystem can be intimidating to much of its target audience, which is both high-level scriptors (using MATLAB/Python/R) all the way to developers of high-performance libraries (using C/Fortran). This workshop is 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.). Near the end of the workshop, participants will break out into groups to solve problems which mirror research problems in data science and scientific computing.
Date:May 24, 2016
Time: 9 am to 1 pm
Location: Donald Bren Hall, Room 3011
Instructor: Chris Rackauckas
Pre-requisites: Previous experience with a scripting language (R/Python/MATLAB etc.).

Registration for this workshop has closed due to capacity constraints. You my still attend as a walk-in on the day of the workshop, as long as there are seats available when the workshop begins. You are also invited to fill out this workshop interest survey.

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Southern California Machine Learning Symposium
2016-05-20 09:00 - 20:00
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The 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.

Full details and registration information are available at
http://www.calit2.uci.edu/calit2-events/calendar.aspx?eid=796

This 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.
