This three-part series will cover the following materials:
Part 1: Introduction
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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.
Workshop: Stata FUN!damentals: Part 2
December 13, 2017
Author:
Jackie Ferguson
Workshop: LaTeX Fundamentals
December 12, 2017
Author:
Isabelle Cohen
LaTeX is a widely used document creation software which can help you improve the presentation of homework, papers, academic articles and even presentations.
Workshop: Stata FUN!damentals: Part 1
December 12, 2017
Author:
Saika Belal
This three-part series will cover the following materials:
Part 1: Introduction
Workshop: Python FUN!damentals: Part 4
December 8, 2017
Author:
Rachel Jansen
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.
Workshop: R FUN!damentals: Part 4
December 8, 2017
Author:
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.
Workshop: Python FUN!damentals: Part 3
December 7, 2017
Author:
Rachel Jansen
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:
December 7, 2017
Author:
Josué Meléndez Rodríguez
Join qualitative and mixed-methods researchers for an informal meet’n’greet at D-Lab. Bring a valid ID with date-of-birth, and enjoy a couple glasses on us!
December 7, 2017
Author:
Ben Gebre-Medhin
In this workshop we will cover two main supervised text analysis methods, the dictionary method, and supervised classification. We will use list comprehension to implement the dictionary method, using sentiment analysis as our example.
December 7, 2017
Author:
Lindsay Bayham
This interactive workshop will discuss how to analyze qualitative data, including how to develop codes, look for patterns, answer research questions, and build an argument in order to write the findings, discussion, and conclusion sections of a research paper. Researchers at any stage in the process are welcome.
Workshop: R FUN!damentals: Part 3
December 7, 2017
Author:
Evan Muzzall
R FUN!damentals Part 3: Data Exploration and Analysis
Students will be introduced to data exploration and analysis in R. You will learn how to summarize data and explore it with histograms, scatterplots, and boxplots. You will also be introduced to coding statistical data analysis via t-tests, analyses of variance, correlation, and linear regression.
Workshop: Python FUN!damentals: Part 2
December 6, 2017
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:
December 6, 2017
Author:
Lindsay Bayham
This workshop focuses on how to organize and code qualitative data in Dedoose. The training will outline key decisions researchers must make in the coding process, as well as review how to start a new project and engage in basic tasks.
December 6, 2017
Author:
Ben Gebre-Medhin
This hands on workshop builds on part 2 by introducing the basics of Python's scikit-learn package to implement unsupervised text analysis methods. This workshop will cover a) vectorization and Document Term Matrices, b) weighting (tf-idf), and c) uncovering patterns using topic modeling.
December 6, 2017
Author:
Zawadi Rucks-Ahidiana
This workshop focuses on how to organize and code qualitative data in MaxQDA. The training will outline key decisions researchers must make in the coding process, as well as review how to start a new project and engage in basic tasks.
Workshop: R FUN!damentals: Part 2
December 6, 2017
Author:
Evan Muzzall
R FUN!damentals Part 2: Subsetting and Reshaping
Workshop: HTML/CSS Toolkit for Digital Projects
December 5, 2017
Author:
Stacy Reardon
If you've tinkered in WordPress, Google Sites, or other web publishing tools, chances are you've wanted more control over the placement and appearance of your content. With a little HTML and CSS under your belt, you'll know how to edit "under the hood" so you can place an image exactly where you want it, customize the formatting of text, or troubleshoot copy & paste issues.
December 5, 2017
Author:
Celia Emmelhainz
This workshop focuses on how to organize and code qualitative data in ATLAS.ti. The training will outline key decisions researchers must make in the coding process, as well as review how to start a new project and engage in basic tasks.
Workshop: Python FUN!damentals: Part 1
December 5, 2017
Author:
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 1 Topics:
December 5, 2017
Author:
Desiree French
This workshop focuses on how to organize and code qualitative data in NVivo. The training will outline key decisions researchers must make in the coding process, as well as review how to start a new project and engage in basic tasks.
December 5, 2017
Author:
Ben Gebre-Medhin
This hands on workshop goes through the common “preprocessing recipe” that is used as the foundation for a variety of other applications as well as some basic natural language processing techniques. These include: a) digitization (utf 8), b) removal of stopwords, numbers, punctuation, c) tokenization, d) calculation of word frequencies / proportions, e) part of speech tagging, and f) concordan
