Tag: Applied Research

Data Science Storytelling: Quantitative UX Research in Google Cloud with Randy Au (Part 2 of 2)

In this second part of my interview with Randy Au, he discusses the techniques he used to teach himself to code and his approach to programming and data science as a social scientist.

Here is Part 1 of our interview.

Prior to joining Google, he spent a decade as a mixture of a data analyst, data scientist, and data engineer at various startups in New York City and before that, studied Communications. In his newsletter, he discusses data science topics like data collection and data quality from a social science perspective. Outside of work he often engages in far too many hobbies, taken to absurd lengths.

Click here to learn more about the Interview Series this is a part of.

More about Randy:

Data Science Storytelling: Quantitative UX Research in Google Cloud with Randy Au (Part 1 of 2)

Randy Au, a Quantitative UX Researcher at Google, explains how he leverages his backgrounds in communication, statistics, and programming as a quantitative UX researcher in Google Cloud to analyze and improve Cloud Storage products.

Here is Part 2 of our interview.

Prior to joining Google, he spent a decade as a mixture of a data analyst, data scientist, and data engineer at various startups in New York City and before that, studied Communications. In his newsletter, he discusses data science topics like data collection and data quality from a social science perspective. Outside of work he often engages in far too many hobbies, taken to an absurd lengths.

Click here to learn more about the Interview Series.

More about Randy:

Designing Machine Learning Products Anthropologically: Building Relatable Machine Learning

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How do we build relatable machine learning models that regular people can understand? This is a presentation about how design principles apply to the development of machine learning systems. Too often in data science, machine learning software is not built with regular people who will interact with it in mind.

I argue that in order to make machine learning software relatable, we need to use design thinking to intentionally build in mechanisms for users to form their own mental models of how the machine learning software works. Failing to include theses helps cultivate the common sense that machine learning is a black box for users.

I gave three different versions of this talk at Quant UX Con on June 8th, 2022, the Royal Institute of Anthropology’s annual conference on June 10th, 2022, and Google’s AI + Design Tooling Research Symposium on August 5th, 2022.

I hope you find it interesting and feel free to share any thoughts you might have.

Thank you for the conference and talk organizers for making this happen, and I appreciate all the insightful conversations I had about the role of design thinking in building relatable machine learning.

Trash Data Science: Garbology, Anthropology, and Spatial Data Science – Conversation with Gideon Singer (Part Four)

Here is the fourth and final part of my interview with Gideon Singer, Director of Spacial Data Science at Litterati, for my Interview Series. He describes the strategies he uses to collect data as a garbologist and data scientist.

Here is Part 1, Part 2, and Part 3 of our interview.

Gideon Singer is an applied anthropologist in the business of exploring societies through the waste, litter, rubbish, and other detritus they leave behind. As a self-proclaimed digital garbologist, his work juxtaposes digital ethnography with archaeology and spatial data science.

Resources:

Trash Data Science: Garbology, Anthropology, and Spatial Data Science – Conversation with Gideon Singer (Part Three)

Here is the third part of my interview with Gideon Singer, Director of Spacial Data Science at Litterati, for my Interview Series. He discusses how the interconnections he has found between data science and garbology.

Here is Part 1, Part 2, and Part 4 of our interview.

Gideon Singer is an applied anthropologist in the business of exploring societies through the waste, litter, rubbish, and other detritus they leave behind. As a self-proclaimed digital garbologist, his work juxtaposes digital ethnography with archaeology and spatial data science.

Resources:

Trash Data Science: Garbology, Anthropology, and Spatial Data Science – Conversation with Gideon Singer (Part Two)

Here is the second part of my interview with Gideon Singer, Director of Spacial Data Science at Litterati, for my Interview Series. He describes garbology is and what kind of work he does as a data scientist garbologist.

Here is Part 1, Part 3, and Part 4 of our interview.

Gideon Singer is an applied anthropologist in the business of exploring societies through the waste, litter, rubbish, and other detritus they leave behind. As a self-proclaimed digital garbologist, his work juxtaposes digital ethnography with archaeology and spatial data science.

Resources:

Trash Data Science: Garbology, Anthropology, and Spatial Data Science – Conversation with Gideon Singer (Part One)

I interviewed Gideon Singer, Director of Spacial Data Science at Litterati, for my Interview Series. He discusses his mission to combine garbology, anthropology, and data science to better understand humanity and the trash we leave behind. In this first part, he describes the connections he has found between these various fields.

Here is Part 2, Part 3, and Part 4 of our interview.

Gideon Singer is an applied anthropologist in the business of exploring societies through the waste, litter, rubbish, and other detritus they leave behind. As a self-proclaimed digital garbologist, his work juxtaposes digital ethnography with archaeology and spatial data science.

Resources:

Applying Computational Ethnography and Statistics to Vapor Wave: Interview with Tanner Greene (Part 2 of 2)

Here is the second part of three in my conversation with Tanner Greene. He discusses his strategies for transitioning from graduate school to UX research and his recommendations for any fellow student seeking to do the same.

Here is Part 1 of our interview.

Tanner Greene is a UX Researcher and Ph.D. Candidate at the University of Virginia, where he’s finishing a dissertation on the history of vaporwave, a music genre created on social media platforms. Tanner’s interests straddle math and the humanities, spanning digital cultures, user metadata, and a long-dormant statistics ability he wants to revive. In his spare time, Tanner enjoys writing about music, playing video games, and dreaming about learning SQL.

Resources We Referenced:

For more context on my interview series in general, click here.

Applying Computational Ethnography and Statistics to Vapor Wave: Interview with Tanner Greene (Part 1 of 2)

For my next installment in my Interview Series, I interviewed Tanner Greene. He recently received his doctorate from the University of Virginia for his research on the digital music genre, vapor wave. He primarily used qualitative means but has also taught himself Python to be able to employ quantitative textual analysis into his project. It is a good example of how to integrate qualitative digital ethnographic techniques with quantitative natural language processing.

In this first part, he discusses why he decided to study the vapor wave community and his experiences learning Python to conduct statistical analysis with.

Here is Part 2 of our interview.

Tanner’s interests straddle math and the humanities, spanning digital cultures, user metadata, and a long-dormant statistics ability he wants to revive. In his spare time, Tanner enjoys writing about music, playing video games, and dreaming about learning SQL.

Resources We Referenced:

For more context on my interview series in general, click here.

Ethno-Data: Introduction to My Blog

            Hello, my name is Stephen Paff. I am a data scientist and an ethnographer. The goal of this blog is to explore the integration of data science and ethnography as an exciting and innovative way to understand people, whether consumers, users, fellow employees, or anyone else.

            I want to think publicly. Ideas worth having develop in conversation, and through this blog, I hope to present my integrative vision so that others can potentially use it to develop their own visions and in turn help shape mine.

Please Note: Because my blog straddles two technical areas, I will split my posts based on how in-depth they go into each technical expertise. Many posts I will write for a general audience. I will write some posts, though, for data scientists discussing technical matters within that field, and other posts will focus on technical topics withn ethnography for anthropologists and other ethnographers. At the top of each post, I will provide the following disclaimers:

Data Science Technical Level: None, Moderate, or Advanced
Ethnography Technical Level: None, Moderate, or Advanced