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.

10/26/18 Intro to Linux on the HPC
2018-10-26 09:00 - 17:00 (America/Los_Angeles) | Donald Bren Hall 4011
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: Friday, October 26, 2018
Time: 9am-5pm (coffee and lunch will be provided)
Location: Donald Bren Hall, Room 4011
Cost:
Register online before 10/26/18: $10
Walk-ins: $15 (cash or check payable to “UC Regents” only)

11/02/18 Topics in R
2018-11-02 09:00 - 17:00 (America/Los_Angeles) | 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.
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.

11/09/18 An Introduction to Julia
2018-11-09 09:00 - 17:00 (America/Los_Angeles) | 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 v1.0 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.).

02/15/19 Introduction to Deep Learning
2019-02-15 09:00 - 17:00 (America/Los_Angeles) | Donald Bren Hall 4011
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.

02/22/19 Intro to Linux
2019-02-22 09:00 - 17:00 (America/Los_Angeles) | Donald Bren Hall 4011
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.

03/01/19 A Review of Graph Convolutional Neural Networks
2019-03-01 09:00 - 17:00 (America/Los_Angeles) | Donald Bren Hall 4011
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.

03/08/19 Intro to Deep Generative Models
2019-03-08 09:00 - 17:00 (America/Los_Angeles) | Donald Bren Hall 4011
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.

Introduction to Data Analysis with R
2020-05-16 - 2020-05-17 | Zoom
Description
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.
Syllabus

Fundamentals of R & RStudio: the basics–including objects, subsetting, indexing, data I/O, and control structures
Exploratory 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
Linear Regression: everything you need to know to begin fitting linear models–from simple t-tests to estimation of regression coefficients, variable selection, and prediction
Logistic 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

Data Exploration and Visualization using R and ggplot2
2020-06-22 | Zoom
About this Event

*Time:* June 22 (Monday) 10AM-12PM, 1PM-3PM, 4PM-5PM (Participants are registered for all three sessions.)
*Name:* Data Exploration and Visualization using R and ggplot2
(Zoom meeting link will be emailed to registered attendees two days prior to the event.)
*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.
*Syllabus:*
*10AM-12PM session:*
* R and RStudio basics: data types, subsetting, reading/writing data, loops and functions.
* Basic data exploration and plotting in base R: summaries, tables, plotting different types of data.
*1PM-3PM Session:*
* Introduction to ggplot2 and the “grammar of graphics:” understanding the logic of ggplot2, preparing and plotting data.
* Beyond ggplot2 basics: overlaying geoms, visualizing strata for comparison, changing default plot options.
*4PM-5PM session:*
* Example: making a choropleth map in ggplot2.
****FOR SPECIFIC COURSE CONTENT QUESTIONS, PLEASE CONTACT COURSE INSTRUCTOR ARNIE SEONG: akseong@uci.edu *****

Experimental Design - The Key to Reliable & Reproducible Science
2020-08-28 10:00 - 17:00 UTC | Zoom, *Link will be emailed to registrants
Who should attend?
Anyone interested in designing, conducting, or analyzing (controlled) experiments to answer scientific questions involving group comparisons.
Description:
Obtaining 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?
Then 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.
This 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.
This short course will be divided into three sessions:
10AM-Noon, 1PM-3PM, 4PM-5PM.

Register Online Now!

Instructor Bio:

Navneet 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.
