D-Lab | Intelligent research design for data intensive social science

John Ashbery, Non-Performer? Prosody and Voice Studies

Invited Lecture

How and much might a poet’s reading style change over time, and how can it be studied? What role might be played by venue, media format, poetic content, authorship, audience, and so on?

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Doing Voice Studies in the Digital Humanities

This training will introduce you to open-source, user-friendly tools for analyzing and visualizing intonation and timing patterns in recordings of performative speech, from poetry readings to political speeches to podcasts. Developed with an ACLS Digital Innovations Fellowship and a NEH Digital Humanities Advancement grant, these tools apply state-of-the-art pitch tracking and a speech recognition toolkit using advanced neural networks. They can help scholars in sound and voice studies test and refine impressions about individual performance styles and changes and trends over time.

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Text Analysis Fundamentals: Supervised Methods

In this workshop we will cover the most common CTA task: supervised classification. Using the Python library scikit-learn, we will implement Logistic Regression and Random Forest methods to perform sentiment analysis. Optional: introduction to word vector representations with Word2Vec.

Prior knowledge: Basic familiarity with Python is required if you wish to follow along with the tutorial. Completion of D-Lab's Python FUN!damentals workshop series will be sufficient.

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Introduction to Deep Learning in R: Part 2

This workshop introduces the basic concepts of Deep Learning - the training and performance evaluation of large neural networks, especially for image classification, natural language processing, and time-series data.

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Introduction to Deep Learning in R: Part 1

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Visualizing Data: Theory and Best Practices

This workshop will cover theory and techniques for maximizing the effectiveness of figures used for visualizing information. Rather than teaching any particular visualization software, this course will teach students about the "nuts and bolts" of effective data visualization.

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Acoustic Data for Linguistic Analysis

Posted: Apr, 01, 2019

By: Emily Grabowski

Acoustic data, or recorded human speech, is often used in data science for direct applications such as automatic speech recognition. However, acoustic data is a rich source of information about human language and has the potential to contribute to research beyond these specific applications.

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Research on Data Science Career Paths / Research IT Reading Group

Research on Data Science Career Paths

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Machine Learning in Python

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.

Prior knowledge: We will assume a basic knowledge of Python and a basic understanding of machine learning techniques. No theory instruction will be provided.

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Text Analysis Fundamentals: Unsupervised Approaches

This hands on workshop builds on part 1 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.