This workshop will demonstrate how to use Matlab to create data visualization and animation for publications or presentations.

The talk is the slide presentation in UCLA OARC/IDRE workshop "Learning PyTorch" in Fall 2021.

Slides and demos can be downloaded from the github repo: https://github.com/huqy/deep_learning_workshops

VIDEO CHAPTERS
00:00 Opening Remarks
01:14 What is PyTorch
06:47 Why PyTorch
14:40 PyTorch Tensors
22:02 Py and Non-Py in PyTorch
28:21 Automatic differentiation
33:06 Optimizers in PyTorch
36:34 Neural networks in PyTorch
44:11 PyTorch, Tensorflow and JAX

This workshop will demonstrate how to use Matlab to create data visualization and animation for publications or presentations.

This is the slide presentation for UCLA OARC/IDRE Workshop "Learning Scikit-Learn: Basics" in Summer 2021.

The pdf slide and the demo files can be downloed from the github repo: https://github.com/huqy/learning-sklearn

Video Chapters:
00:00 Opening remarks
03:25 Knowing scikit-learn in minutes
12:21 Simplified workflow for a machine learning project
15:09 Data input, loader and generator
20:20 Splitting the data
24:51 Scikit-learn core objects
29:11 Data pre-processing
32:08 Models and meta-estimators
43:35 Model persistence
48:29 Scikit-learn extension libraries
53:32 Key Takeaways

IDRE Workshop from August 6, 2021
Materials available here: https://github.com/benjum/idre-intro-to-shell-scripting

IDRE Workshop from July 30, 2021
Materials available here: https://github.com/benjum/idre-intro-to-shell-cli

IDRE Workshop from January 15, 2021
Materials available here: https://github.com/benjum/numerical-python-part2

IDRE Workshop from January 8, 2021
Materials available here: https://github.com/benjum/numerical-python-part1

IDRE Workshop from January 22, 2021
Materials available here: https://github.com/benjum/numerical-python-part3

Video contents:
0:00:00 Introduction
0:18:46 Secure shell (ssh)
0:26:28 ssh key pair: demo
0:34:05 Linux Shell environment
0:46:49 Vim text editor
1:00:54 Vim: basic editing
1:09:10 Vim: browsing large code base
1:15:05 Git version control
1:23:04 Git merge conflict resolution using Vim
1:29:39 Git rebase using Vim: explanation
1:36:06 Git rebase using Vim: demo
1:39:02 Tmux: terminal multiplexer

Supplementary course materials:
https://github.com/schuang/essential-command-line-tools-tutorial

Questions and comments:
https://github.com/schuang/essential-command-line-tools-tutorial/issues

* We are in the process of fixing the wrong words in the subtitles. Thank you for your understanding.

Instructor: Yoh Kawano

Workshop materials: https://github.com/benjum/idre-data-sci-with-python

Part 2: A Data Science case study: 3D Mapping of Los Angeles arrest data

- libraries to include: sodapy, pandas, plotly, keplergl

Machine learning with iris dataset

- examples with pytorch and tensorflow

Workshop material: https://github.com/benjum/idre-data-sci-with-python

Part 1: Introducing data science with python using the iris dataset to cover topics for getting, cleaning, and visualizing data.

libraries to include: openpyxl, csv, json, requests, urllib, pandas, matplotlib, seaborn, plotnine, plotly, altair

IDRE Workshop from July 28, 2020
Materials available here: https://github.com/benjum/idre-intro-to-sql

Cloud computing service is the delivery of computing resources including applications, servers, storage, databases, networking, analytics, and artificial intelligence over the internet. This workshop series is intended to provide hands-on tutorials for academic researchers to use cloud computing resources from two major cloud providers, Amazon Web Service and Google Cloud Platform.

Cloud computing service is the delivery of computing resources including applications, servers, storage, databases, networking, analytics, and artificial intelligence over the internet. This workshop series is intended to provide hands-on tutorials for academic researchers to use cloud computing resources from two major cloud providers, Amazon Web Service and Google Cloud Platform.

Cloud computing service is the delivery of computing resources including applications, servers, storage, databases, networking, analytics, and artificial intelligence over the internet. This workshop series is intended to provide hands-on tutorials for academic researchers to use cloud computing resources from two major cloud providers, Amazon Web Service and Google Cloud Platform.

Anaconda3 is comprehensive and versatile solution for scientific computing and data analysis. It provides a free and open-source platform that supports many packages written in different languages. In this workshop, we will explore how to use Anaconda3 to install and manage these packages efficiently and effectively.

Speaker: Seyoon Ko, Ph.D.
IDRE Fellow and Assistant Adjunct Professor,
Mathematics Department,
University of California Los Angeles

Abstract: Julia (http://julialang.org) is a modern open-source programming language for technical computing. Its design offers much greater speed and productivity compared to R or Python, as high-performance code does not need to be wrapped in a low-level language like C or Fortran. After almost a decade of active development, Julia reached its first major release, v1.0, in 2018, and is quickly gaining popularity in scientific computing and data science communities. In this workshop, I will present the basic concepts of Julia and show a little comparison between Julia and other languages, such as R, C, and Python.

The IDRE Early Career Researchers group's panel is a discussion on how high-performance computing (HPC) is used across STEM disciplines. It is led by the following new cohort of IDRE fellows (2023-2024) from various disciplines across the UCLA campus.

Robert Fofrich – Institute of the Environment and Sustainability
Fei-man Hsu – Department of Molecular, Cell, and Development Biology
Alec Linot – Mechanical and Aerospace Engineering
Xiao Luo – Department of Computer Science
Namjoon Suh – Department of Statistics and Data Science

Thursday, October 14, 2021 from 12-2pm

Scholarship of visual materials requires close looking and comparison across works. Digitization efforts across the world have allowed for many digital facsimiles of these materials to be made available online. For scholarship, digital viewers are extremely valuable to presenting high-resolution imaging, which allows for close examination of materials. Interoperability has become crucial for institutions for contrasting objects from different collections. Increasing stability in digital visual scholarship means working from authority files from museums, libraries, and archives — institutions are committed to long term preservation and standards.

This workshop will introduce participants to Mirador, an “open-source, web based, multi-window image viewing platform with the ability to zoom, display, compare and annotate images from around the world.” Leveraging the International Image Interoperability Framework (IIIF), the Mirador web-viewer allows users to compare and annotate images and documents. Participants will learn how to install a local instance of Mirador, find Mirador-friendly content and resources, make narratives with annotations, and complete side-by-side transcriptions.

All levels are welcome.

Augmented reality (AR) is the layering of computer-generated perceptual information onto real environments. The experience requires a level of interactivity and sensory engagement. AR is being adopted across the fields of architecture and engineering, as well as adopted by artists to make creative connections between our shared reality and that of innumerable virtual worlds.

This workshop will introduce participants to the design and mechanics of augmented reality. We will focus on working with images, bringing digital objects into live environments through the use of the ArtVive app. Attendees will create free Artivive accounts and learn to create an AR animation. No prior technical experience necessary.

IDRE Workshop from April 24, 2021
Materials available here: https://github.com/benjum/idre-spring21-python-data-viz-2

IDRE Workshop from April 16, 2021
Materials available here: https://github.com/benjum/idre-spring21-python-data-viz-1

3D models can be formed in a number of ways. Two common surveying methods that result in 3D models are photogrammetry and light detection and ranging or lidar for short.

A form of photogrammetry has been around since the beginnings of photography in the mid-19th century. The term usually refers to the method by which 2D images are compared and combined using optics, projective geometry, and computing to produce a 3D model. This method of image overlap to create a dimensional model is applied in many object-oriented disciplines such as archeology, topographic mapping, architecture, engineering, manufacturing, quality control, police investigation, cultural heritage, and geology.

Lidar is a method for measuring distances (ranging) by illuminating the target with laser light and measuring the reflection with a sensor. Differences in laser return times and wavelengths can then be used to make digital 3D representations of the target. While long used in the earth sciences, lidar is becoming increasingly available to all as these sensors are being added to cellular mobile devices, increasing its academic and commercial potential.

In this workshop, participants will learn about the hardware and software required for these methods. We will also cover the workflow that is involved in producing photogrammetric and lidar 3D models. All levels welcome.

If you have any further questions regarding the workshop, please contact the instructor, Dr. Francesca Albrezzi at falbrezzi@ucla.edu

While we are working remotely, there are many ways that we can share virtual space. Mozilla Hubs is a Web-based platform that provides users with three dimensional virtual space that can allow for links, videos, 2D and 3D objects, and live interaction. As an alternative to zoom, Mozilla Hubs has been used for poster sessions, conferences, performances, exhibitions, and more. It can also be accessed through a virtual reality headset for an immersive experience.

In this workshop, participants will be introduced to Mozilla Hubs and how to use the built-in functionality. For more customized environments, we will also cover how to build your own basic environment and bring it into Mozilla Hubs through Spoke, Mozilla Hub’s web editor.



If you have any further questions regarding the workshop, please contact the instructor, Dr. Francesca Albrezzi at falbrezzi@ucla.edu.

https://idre.ucla.edu/calendar-event/creating-vr-environments-mozilla-hubs-and-blender-basics

August 11, 2020 @ 2:00 pm - 4:00 pm

We are increasingly sharing our data and research in virtual environments. WebVR (or WebXR as it’s now known) is an open standard that allows 3D / virtual reality (VR) experiences to be produced through your browser. It also aims to make your immersive data platform-agnostic, so it will be portable to whatever device you have. In this workshop, you will be introduced to ways to graph three dimensional data. You will also be shown how to bring that 3D graph into a browser environment so that your work can be shared through the Web.In the hands-on segment of the workshop, we will be using the following tools:
R
A-Frame
Jupyter Notebooks
There are no pre-requisite requirements to take this workshop.


If you have any further questions regarding the workshop, please contact instructor Francesca Albrezzi.

Workshop by Francesca Albrezzi, PhD
Credit to Miriam Posner, PhD for several slides from her DH101 lectures

Workshop slides: http://bit.ly/DataVizSlidesF2020

TOC
3:56: Overview on Data Types and Visualization Methods
31:33 Open Refine Reconciliation Using the Getty Vocabularies
1:08:33 Google Pivot Tables
1:17:30 Tableau

The digital realm is becoming an increasingly popular space for creatives. By creatives, I don't just mean artists, but anyone who wants to harness their creativity, skill, and imagination to produce something with a personal touch and flair. From interactivity to detailed design, browser windows can connect with viewers in powerful ways. So how do you start making the computer screen your canvas? It starts with programming.

Programming is writing instructions that computers can understand and execute. For more than fifty years, computer programmers have been writing code and there are now more than 2,500 documented programming languages. So which language do you start with? What are the differences?

In this workshop, you will receive an introduction to the basics of programming. Specifically, we will cover:

-What is programming
-What is processing
-The difference between Low-Level vs High Level programming languages
-The difference between Client Side vs. Service Side Programs
-p5.js, a free and open-source JavaScript library for creative coding, with a focus on making coding accessible and inclusive for artists, designers, educators, and beginners
-How to use the p5.js web editor
-How to create shapes and draw using p5.js

All levels welcome and no experience required.

If you have any further questions regarding the workshop, please contact the instructor, Dr. Francesca Albrezzi.

Instructor: Yoh Kawano
Course material:
https://github.com/yohman/workshop-leaflet

Course setup:
https://github.com/yohman/workshop-leaflet/blob/main/setup.md

Course lab:
https://github.com/yohman/workshop-leaflet/blob/main/lab.md

Research projects are often confronted with the need to publish results to communicate their findings to the general audience. Publishing research on an interactive web-based map is an effective and visually compelling way to do so.

In this workshop, participants will learn the basics of HTML, CSS and Javascript. We will use the Leaflet library to overlay research data and create compelling narrative maps. Finally, participants will learn how to use GitHub Pages as a publishing platform for the interactive maps.

Yoh Kawano Ph.D., Lead Computational Scientist, GIS/Spatial Data Science - UCLA Office of Advanced Research Computing

Geospatial platforms have dramatically evolved over the past two decades. What's the human story and impact behind them? This talk will highlight the history of innovative and influential projects designed through academic, government, and community partnerships at UCLA. It will discuss the impact they have had upon our communities and how they address the digital divide (e.g. Neighborhood Knowledge Los Angeles, HyperCities, Million Dollar Hoods). The talk will also highlight a personal journey into spatial ethnography, and how in-depth, on-the-ground fieldwork can address healing in a risk society and illuminate the narratives of people affected by catastrophic disaster situations.

Instructor: Yoh Kawano
Workshop materials: https://github.com/yohman/workshop-remote-sensing

Satellites are circling our planet, allowing us to "sense" things about the Earth. It is the art and science of making measurements using sensors. Remote sensing has thus become a valuable tool in research and applications in a wide range of disciplines, such as engineering, geology, geography, urban planning, public health, archeology, environmental studies, disaster research, forestry, and agriculture.

In this workshop, you will:

- learn the basic principles of remote sensing methods in research
- have a hands-on session on using Jupyter Notebooks to code in Python
- import Landsat imagery using Google Earth Engine's Python library
- conduct a basic NDVI (Normalized Difference Vegetation Index) analysis

Instructor: Yoh Kawano

In this hands on workshop, you will be provided with a Jupyter Notebook workspace to import census data, explore, clean, and prepare the data for analysis using python. You will also learn how to create compelling choropleth maps with selected variables that can be used for publications.

Course materials:

https://github.com/yohman/workshop-census

Workshop materials available here:

https://github.com/yohman/workshop-python-spatial-stats

Visual interpretations are meaningful ways to determine spatial trends in our data. However, underlying factors—such as inconsistent geographies, scale, data gaps, overlapping data—have the potential to produce incorrect assumptions, as valuable information may be conveniently hidden from the visual output.

One way to address this issue is to amend your visual output with geo-statistical validation. In this workshop, we will use Python to look at one such approach: Spatial Autocorrelation.

Instructor: Yoh Kawano
Course materials: https://github.com/yohman/getting-started-with-gis

In this hands on workshop, you will be provided with a Jupyter Notebook workspace to import census data, explore, clean, and prepare the data for analysis using python. You will also learn how to create compelling choropleth maps with selected variables that can be used for publications.

Workshop material: https://github.com/benjum/idre-data-sci-with-python

Part 1: Introducing data science with python using the iris dataset to cover topics for getting, cleaning, and visualizing data.

libraries to include: openpyxl, csv, json, requests, urllib, pandas, matplotlib, seaborn, plotnine, plotly, altair

Instructor: Yoh Kawano
Course materials: https://github.com/IDREsandbox/gisworkshop

As members of an academic community with a myriad of research initiatives, the need to map information, to spatially analyze, to geoprocess, and to visualize space and time is becoming ubiquitous. But how do you get started? The answer to this question largely depends on what you want to map, and how you want to create your map. The what is usually easy. It is driven by your passion to pursue a research question and to have maps assist in the development of an argument. The how is the difficult part. It may depend on what data you have, what tools you have access to, how comfortable you are with software packages, if you are open to write code or use the command line. Often times, your research question lends itself to a spatial argument. However, providing insights to questions using spatial visualization tools is a process that involves any number of factors, including: data acquisition, data cleanup, geo-enabling data, geocoding data, georeferencing data, visualizing spatial data, overlaying other spatial data, conducting spatial analysis and/or geoprocessing, analyzing results, visualizing results, and publishing results.

In the hands-on segment of the workshop, we will be using the following tools to learn how to analyze and map COVID-19 data:

GitHub
Python
Jupyter Notebooks

Instructor: Yoh Kawano

Workshop materials: https://github.com/benjum/idre-data-sci-with-python

Part 2: A Data Science case study: 3D Mapping of Los Angeles arrest data

- libraries to include: sodapy, pandas, plotly, keplergl

Machine learning with iris dataset

- examples with pytorch and tensorflow

Introduction to SPSS Seminar given on 10/26/2020

Introduction to SEM seminar originally given on February 22, 2021.

This is the second seminar in a three-part series.

1. Confirmatory Factor Analysis (CFA) in R with lavaan
https://stats.idre.ucla.edu/r/seminars/rcfa/

The first seminar introduces the confirmatory factor analysis model, and discusses model identification, degrees of freedom and model fit.

2. Introduction to Structural Equation Modeling (SEM) in R with lavaan
https://stats.idre.ucla.edu/r/seminars/rsem/

The second seminar explores structural equation models which is an umbrella term that encompass linear regression, multivariate regression, path analysis, CFA and structural regression.

3. Latent Growth Models (LGM) and Measurement Invariance with R in lavaan
https://stats.idre.ucla.edu/r/seminars/lgm/

The third seminar introduces latent growth modeling and how it relates to hierarchical linear models (HLM) and 2) measurement invariance in CFA and how to compare model fit between invariance models.

Confirmatory Factor Analysis in R with lavaan workshop given at UCLA on May 17, 2021 by Johnny Lin, Ph.D.

This is the first seminar in a three-part series.

1. Confirmatory Factor Analysis (CFA) in R with lavaan
https://stats.idre.ucla.edu/r/seminars/rcfa/

The first seminar introduces the confirmatory factor analysis model, and discusses model identification, degrees of freedom and model fit.

2. Introduction to Structural Equation Modeling (SEM) in R with lavaan
https://stats.idre.ucla.edu/r/seminars/rsem/

The second seminar explores structural equation models which is an umbrella term that encompass linear regression, multivariate regression, path analysis, CFA and structural regression.

3. Latent Growth Models (LGM) and Measurement Invariance with R in lavaan
https://stats.idre.ucla.edu/r/seminars/lgm/

The third seminar introduces latent growth modeling and how it relates to hierarchical linear models (HLM) and 2) measurement invariance in CFA and how to compare model fit between invariance models.

Workshop given on August 16, 2021 by Johnny Lin, PhD
UCLA OARC IDRE Statistical Consulting

This is the third seminar in a three-part series.

1. Confirmatory Factor Analysis (CFA) in R with lavaan
https://stats.idre.ucla.edu/r/seminars/rcfa/

The first seminar introduces the confirmatory factor analysis model, and discusses model identification, degrees of freedom and model fit.

2. Introduction to Structural Equation Modeling (SEM) in R with lavaan
https://stats.idre.ucla.edu/r/seminars/rsem/

The second seminar explores structural equation models which is an umbrella term that encompass linear regression, multivariate regression, path analysis, CFA and structural regression.

3. Latent Growth Models (LGM) and Measurement Invariance with R in lavaan
https://stats.idre.ucla.edu/r/seminars/lgm/

The third seminar introduces latent growth modeling and how it relates to hierarchical linear models (HLM) and 2) measurement invariance in CFA and how to compare model fit between invariance models.

Introduction to the analysis of complex survey data in Stata 17.

This workshop describes how to conduct various types of mediation analyses using the PROCESS macro in SPSS.

Workshop discussing ways to present statistical results in peer-reviewed journal articles.

Workshop teaching basic usage of the ggplot2 package in R.

Workshop teaching basics of R Markdown.

This workshop is an introduction to Mplus 8.8. Topics covered include how to get data into Mplus, the basics of Mplus syntax, and how to run basic models in Mplus.

This workshop goes beyond the basics of SPSS syntax so that researchers can accomplish data management tasks via SPSS syntax. Most of the data management commands in SPSS are discussed.

This workshop demonstrates the use of the PROCESS macro in SPSS to graph interaction terms. You will learn how to interpret the coefficients of the interaction terms and the lower-order effects, how to probe the interaction and how to graph it.

Introduction to survival analysis in R using the 'survival' package.

Interactive workshop introducing R.
Webpage: https://stats.oarc.ucla.edu/r/seminars/intro/

Analysis and Visualization of Interactions in R
Webpage: https://stats.oarc.ucla.edu/r/seminars/analysis-and-visualization-of-interactions-in-r/

In this workshop we will discuss missing data in R and introduce some useful packages in R for handling missing data. In particular, we will focus on multiple imputation and how to perform it using the R package, mice: “Multivariate Imputation by Chained Equations”.

In this seminar we discuss generalized linear models and why and when we need to use them. We will discuss several generalized linear modes such as logistic, Poisson, and negative binomial and how we run them in R.

How to create a SAS profile and launch SAS OnDemand.

This workshop introduces the ideas used in meta-analysis, including how to collect and organize data, how to run a basic meta-analysis, and how to interpret the output.