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This is an archive of our past training offerings. We are looking to include workshops on topics not yet covered here. Is there something not currently on the list? Send us a proposal.

E.g., 24-Sep-17
E.g., 24-Sep-17
September 22, 2017
Coordinator:
Chris Gagne

This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets.

September 22, 2017
Coordinator:
Evan Muzzall

R FUN!damentals Part 2: Subsetting and Reshaping

September 21, 2017
Coordinator:
Saika Belal

This three-part series will cover the following materials:

Part 1:  Introduction (Thursday, September 7)

September 21, 2017
Coordinator:
Chris Kennedy

Machine learning often evokes images of Skynet, self-driving cars, and computerized homes. However, these ideas are less science fiction as they are tangible phenomena that are predicated on description, classification, prediction, and pattern recognition in data.

September 20, 2017

This free webinar will provide an overview demonstration of key functionality of NVivo software for qualitative research. Topics to be covered in the webinar include:

September 20, 2017
Coordinator:
Alex Estes

This four-part, interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.

Part 3 Topics:

September 20, 2017
Coordinator:
Janet Torres

This workshop will introduce students to QGIS, a free and open source desktop software tool for working with geographic data. We will use geographic and tabular data from the US Census to demonstrate the functionality of QGIS as well as to discuss GIS (geographic information systems) in general. Participants will learn how to load data in CSV and ESRI shapefile formats into QGIS.

September 19, 2017
Coordinator:
Alex Estes

Students will learn the basics of cleaning, transforming, and formatting text data. They will pull specific elements out of text strings, and pull simple metrics from text data, such as word counts, syntax quantification via part of speech (POS) tagging, and sentiment polarity. Students will be introduced to topic modeling and word2vec methods.

September 19, 2017
Coordinator:
Evan Muzzall

Machine learning often evokes images of Skynet, self-driving cars, and computerized homes. However, these ideas are less science fiction as they are tangible phenomena that are predicated on description, classification, prediction, and pattern recognition in data.

September 18, 2017
Coordinator:
Kari Peterson

Learn data visualization best practices to take your information communication to the next level. We'll cover terminology, tips for general approach to decision making using data, how metaphor influences visualization, basic color theory, and how to convert qualitative information into meaningful metrics.

Prior knowledge: No prior knowledge required

September 18, 2017
Coordinator:
Evan Muzzall

Data are the foundations of the social and biological sciences. Familiarizing yourself with a programming language can help you better understand the roles that data play in your field. Learn to develop and train your data skills at the free D-Lab R workshops!

September 15, 2017
Coordinator:
Evan Muzzall

This workshop will provide a comprehensive overview of graphics in R, including base graphics and ggplot2. Participants will learn how to construct, customize, and export a variety of plot types in order to visualize relationships in data.

September 14, 2017
Coordinator:
Saika Belal

This three-part series will cover the following materials:

Part 1:  Introduction (Thursday, September 7)

September 14, 2017
Coordinator:
Evan Muzzall

R FUN!damentals Part 4: For-loops and Functions

Students will learn how to write for-loops and functions in R. You will learn how to personalize functions via control structures such as ‘if’ and ‘else’. These learning objectives will be exemplified through introduction to the construction and graphical representation of Monte Carlo resampling simulation.

September 13, 2017
Coordinator:
Stacy Reardon

How do you go about publishing a digital book, a multimedia project, a digital exhibit, or another kind of digital project? In this workshop, we'll take a look at use cases for common open-source web platforms WordPress, Drupal, Omeka, and Scalar, and we'll talk about hosting, storage, and asset management. There will be time for hands-on work in the platform most suited to your needs.

September 13, 2017
Coordinator:
Josué Meléndez Rodríguez

This workshop offers a very basic introduction to qualitative research. First, an overview of the qualitative research process will be presented. Then, attendees will briefly explore (a) philosophical and conceptual considerations regarding research, (b) qualitative methodologies and methods, and (c) ways in which technology can aid the qualitative research process.

September 13, 2017
Coordinator:
Alex Estes

This four-part, interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.

Part 2 Topics:

September 13, 2017
Coordinator:
Susan Powell

This workshop will introduce the ArcGIS Online (AGOL) platform. AGOL is a web-based mapping software that allows you to build maps and explore data online. Topics to be covered include how to construct a simple web map from a spreadsheet of data, perform basic spatial analysis and queries, and publish the map to the web.

September 12, 2017
Coordinator:
Quinn Dombrowski, Stacy Reardon

Getting research materials in a digital form that you can search and computationally analyze can be a time-consuming initial step in the research process. While Adobe Acrobat can do basic optical character recognition (OCR, transforming an image of a text into editable text), it performs poorly on documents with complex layouts or non-English text.

September 12, 2017
Coordinator:
Evan Muzzall

R FUN!damentals Part 3: Data Exploration and Analysis

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