training

broom lab methods mini-courses

SPRING 2020
Sarah Alami
(Anthropology)
Introduction to R
When: Monday April 6, 3-5pm
Where: 1053 North Hall
(Broom Computer Lab)
This workshop will provide a basic introduction to using the R program for statistical analysis. 

Jason Budge (Sociology)
Multilevel Modeling.
When: Monday April 20, 1-3pm
Where: 1053 North Hall
(Broom Computer Lab).
This workshop will introduce participants to multilevel modeling (aka hierarchical linear modeling/mixed effects modeling). It will offer both a conceptual overview and hands on application in Stata/R.

WINTER 2020
Amanda Pinheiro (Global Studies)
Ethnographic Methods 
When: Wednesday February 26, 12-2pm
Where: 1053
North Hall (aka the Broom Lab).

SPRING 2019
Vania Wang (Geography)
Social Network Analysis
When: Monday, June 10, 10:00-12:00pm 

Where: Building 434, Room 122 - IGERT Network Science Lab.

Devin Cornell (Sociology)
Text Analysis Workshop
When: Wednesday, June 12,
 12:00-2:00pm
Where: Building 434, Room 122
- IGERT Network Science Lab.

SPRING 2018
Bo Yan
(Geography)
Introduction to sentiment analysis using Word2Vec
When: Wednesday May 23, 3:00-5:00pm
Where: iSpatial Lab (Ellison Hall 2616)

A 2-hour mini-course that introduces the famous Word2Vec model and provides hands-on experience in utilizing Word2Vec in machine learning models to do sentiment analysis. The tutorial will include a brief introduction to the core idea of distributional semantics in natural language processing and computational linguistics, will help you set up visualization tools to explore the semantics of words, and will provide step-to-step instruction on using machine learning models. Prior Python and machine learning experience will be helpful but not required.

WINTER 2018
Karly Miller
 (Marine Science)
Intro to Survey Design & Data Entry with KoBo Toolbox
When: Friday Feb 2, 1:00-3:00pm
Where: Thormahlen Room (North Hall 2111)

When: Friday Feb 9, 1:00-3:00pm
Where: Sycamore Room (Bren Hall 1510)
This 2-hour mini course will introduce KoBo Toolbox, a free survey platform with online/offline capabilities, many question types, and flexible collection options. This tool is a useful addition for anyone who uses or plans to use surveys in their work - it can be used for online survey collection, field data collection, or data entry of paper surveys. The course will begin with an overview of the user interface and platform capabilities, a discussion of trade-offs in data collection methods, and will end with a minilab. No previous experience necessary. If possible/applicable, bring a laptop and a printed version of a survey that you have or are planning to work with (not required!)

 

WINTER 2017
Karly Miller, (IGP Marine Science | Bren School & Geography)
Intro to Survey Design & Data Entry with KoBo Toolbox
When: Friday, February 2, 1:00pm-3:00pm
Where: Thormahlen Room (North Hall 2111)

When: Friday, February 9, 1:00pm-3:00pm
Where: Sycamore Room (Bren Hall 1510)

Spacial Data Analusis with Python Scripts
When: Wednesday, February 23, 1:00-3pm
Where: Ellison Hall 3620 (Descartes lab)
Instructor: Song Gao, Department of Geography
A 2-hour mini-course that introduces key concepts on Python script programming and how it supports the spatial data analysis in ArcGIS environment and in opensource GIS platforms. Examples provided on the analysis of census population & income, GIS datasets and land use datasets from remote sensing imageries.

FALL 2016

Introduction to ATLAS.ti
When: Friday, October 21, 12:00-2pm; 2:00-4:00pm
Where: Social Sciences & Media Studies Building Rm 1301
Instructor: Corrie GrosseDepartment of Sociology
A 2-hour mini-course on uploading, coding, and analyzing qualitative data with ATLAS.ti. Prior experience with ATLAS.ti not required.

SPRING 2016

Geographic Information Systems - Learn ArcGIS and More
When: Tuesday, May 17, 1:00-3:00pm 
Where: Ellison Hall 3620 (Descartes lab)
Instructor: Carlos BaezDepartment of Geography
This 2-hour mini-course will introduce basic understanding of geographic information systems (GIS) and how they can be used to analyze the spatial dimensions of data. Topics covered include: the basics of Geographic systems and information, fundamental techniques used in spatial analysis, and various applications of GIS in demography related research. Participants will learn: the basics of ArcGIS, the most commonly employed full-capability GIS; where to obtain spatial data for research; fundamental spatial analysis techniques; and how to explore geographic information through visualization.

Web Scraping with Python
When: Tuesday, May 17, 3:00-5:00pm
Where: Ellison Hall 2616 (The Spatial lab)
Instructor: Alex Wood-Doughty, 
Department of Economics
A 2-hour mini-course on using Python to acquire data through APIs and through web scraping. Previous experience with Python is not required. 

WINTER 2016

Geographically Weighted Regression (GWR) using ArcGIS
When: Wednesday, January 27th 10am12pm
Where: North Hall 1053 (Broom lab)
Instructor: Kevin Mwenda 
Department of Geography
A 2-hour mini-course on designing, running and analyzing GWR models in ArcGIS. Prior experience with ArcGIS not required but some basic background on linear regression would be beneficial. 

Spatial data visualization in R
When: Friday, February 12th 1 – 3pm
Where: Ellison Hall 2616 (The Spatial lab)
Instructor: Kevin Mwenda 
Department of Geography
A 2-hour mini-course introducing R coding for visualizing & manipulating spatial data. Prior experience with R is not required.

FALL 2015

Remote Sensing and Demography - Learn ENVI
When: Wednesday, November 18th 2-4pm
Where: Ellison Hall 3620 (Descartes lab)
Instructor: Cascade Tuholske
Department of Geography
This mini-course will introduce basic understanding of remote sensing and how it is applied to demography. Topics to be covered are:  types of sensors, what we use satellites to monitor and map (and limitations), and specific applied techniques to demographic questions (including examples from population modeling, human-environment interaction, urbanization and epidemiology). Participants will gain a basic understanding of ENVI, a commonly used remote sensing software package and how to retrieve satellite images from USGS.
45m theory lecture
30m minutes covering ENVI
45m mini-lab

Geographic Information Systems  - Learn ARCGIS and More
When: Friday, November 20th 10am-12pm
Where: Ellison Hall 2616 (The Spatial lab)
Instructor: Carlos Baez
Department of Geography
This mini-course will introduce basic understanding of geographic information systems (GIS) and how they can be used to analyze the spatial dimensions of data. Topics covered include: the basics of Geographic systems and information, fundamental techniques used in spatial analysis, and various applications of GIS in demography related research. Participants will learn: the basics of ArcGIS, the most commonly employed full-capability GIS; where to obtain spatial data for research; fundamental spatial analysis techniques; and how to explore geographic information through visualization.    
45m theory lecture
50m minute walk through lab with ArcGIS
25m minute introduction to visualizing and exploring geographic information 

SPRING 2015

Introduction to Stata
When: Friday, April 10th, 12 – 2pm
Where: North Hall 1053 (Broom lab)
Instructor: Valerie BostwickDepartment of Economics

Geographic Information Systems – Learn ARCGIS and More
When: Friday, April 17th, 1 – 3pm
Where: North Hall 1053 (Broom lab)
Instructor: 
Carlos Baez, Department of Geography

Introduction to and Intermediate Topics in Atlas.ti
When: Friday, May 22nd, 1 – 3pm
Where: North Hall 1053 (Broom lab)
Instructor: Heather HurwitzDepartment of Sociology

Visualizing Spatial Data using R
When: Thursday, May 28th, 9 – 11am
Where: North Hall 1053 (Broom lab)
Instructor: Kevin Mwenda, Department of Geography

R for SPSS Users 
When: Monday, June 1st, 3 – 5pm
Where: North Hall 1053 (Broom lab)
Instructor:   Anne Pisor, Department of Anthropology

WINTER 2015

Introduction to Atlas.ti
When: Wednesday, January 28th, 9 – 10:30AM
Where: North Hall 1053 (Broom lab)
Instructor: Heather HurwitzDepartment of Sociology

FALL 2014

U.S Census Data Acquisition & Visualization
When: Wednesday, November 19th, 10am-12pm
Where: North Hall 1053 (Broom lab)
Instructor: Kevin Mwenda, Department of Geography
This course covered how to download and compile geographic and demographic data from the U.S. Census Bureau website and how to visualize & manipulate this data using ArcGIS.

Introduction to Stata 
When: Friday, December 5th, 1 – 3pm
Where: North Hall 1053 (Broom lab)
Instructor: Valerie BostwickDepartment of Economics
This course covered how to import, clean, and manipulate data in the Stata environment as well as basic statistical analyses such as correlations, regressions, and t-tests. 

SPRING 2014

U.S Census Data Acquisition & Visualization
When: Monday, May 12th, 10am-12pm
Where: North Hall 1053 (Broom lab)
Instructor: Kevin Mwenda, Department of Geography
This course covered how to download and compile geographic and demographic data from the U.S. Census Bureau website and how to visualize & manipulate this data using ArcGIS.

Scraping data from the Web
When: Thursday, May 29th, 1 – 3pm
Where: North Hall 1053 (Broom lab)
Instructor: Miguel Delgado Helleseter, Department of Economics

FALL 2013

Google Data
When: Monday, October 28th (2 – 3:30pm) and Thursday, October 31st (2:30 – 4pm)
Where: North Hall 1053 (Broom lab)
Instructor:  Anand Shukla, Department of Economics

R for SPSS Users
When: Thursday, November 14th, 3 – 5pm
Where: Phelps Hall 1526
Instructor: Anne Pisor, Department of Anthropology

SPRING 2013

Using ArcGIS to perform Geographically Weighted Regression (GWR)
When: Monday, May 20th (10am - 12pm) and Friday, May 24th (11:00 am – 1:00pm)
Where: North Hall 1053 (Broom lab)
Instructor: Kevin Mwenda, Department of Geography
GWR is a localized linear regression used for modeling spatially-varying relationships and ArcGIS happens to have a wonderful tool for getting this done. Apart from demonstrating how GWR may be applied in a real-world example, we also discussed considerations to keep in mind when setting up and analyzing the model output.