Tag: UX 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.

Data Science and Game Design: Conversation with Clayton Sisson (Part 3 of 3)

During the final part of our conversation, Clayton discusses his journey from game design to data science, including what inspired them to study data science and what it has been like learning and working in this new field. Clayton Sisson is a game designer and aspiring data scientist, passionate about how data science can shed light on human behavior.

This is the next installment in my Interview Series. During Over the course of the three parts of our conversation, we discuss how game design thinking can help develop usable and useful machine learning products within data science.

Here is Part 1 and Part 2 of our interview.

Resources:

Data Science and Game Design: Conversation with Clayton Sisson (Part 2 of 3)

In Part 2, we discuss how to apply the design concept shikake to machine learning systems. Clayton Sisson is a game designer and aspiring data scientist, passionate about how data science can shed light on human behavior.

This is the next installment in my Interview Series. During Over the course of the three parts of our conversation, we discuss how game design thinking can help develop usable and useful machine learning products within data science.

Here is Part 1 and Part 3 of our interview.

Resources:

User-Centric Thinking in Data Science: Conversation with Anna Wu at Google Cloud (Part 3 of 3)

I interviewed Anna Wu, a UX researcher and data scientist overseeing Google Cloud’s Compute Engine. In this final part of the conversation, we discuss how design thinking may useful within data science and machine learning.

Here is the first interview if you would like to start from scratch, and here is more information about Interview Series that this is a part of.

Here is Part 1 and Part 2 of our interview.

Anna Wu, established leader in building and leading high-performing data teams to drive changes impacting hundreds of millions of users. Currently as a research manager at Google, she leads a team of quantitative UX researchers applying UX methods and large scale analytics to inform Cloud product development. 

Before this recent chapter, Anna had 10+ years practicing UX and data science at top IT companies and research labs as a UX researcher, data scientist, research scientist at Microsoft, IBM Research and Palo Alto Research Center. She got her PhD in HCI from Penn State and master/bachelor degrees from Tsinghua University.

Resources:

User-Centric Thinking in Data Science: Conversation with Anna Wu at Google Cloud (Part 1 of 3)

I interviewed Anna Wu, a UX researcher and data scientist overseeing Google Cloud’s Compute Engine, as the next installment of my Interview Series,. In this first part of our conversatoin, she discusses her journey from mechanical engineering into UX research and data science and the importance of effective storytelling within these two fields.

Here is Part 2 and Part 3 of our interview.

Anna Wu, established leader in building and leading high-performing data teams to drive changes impacting hundreds of millions of users. Currently as a research manager at Google, she leads a team of quantitative UX researchers applying UX methods and large scale analytics to inform Cloud product development. 

Before this recent chapter, Anna had 10+ years practicing UX and data science at top IT companies and research labs as a UX researcher, data scientist, research scientist at Microsoft, IBM Research and Palo Alto Research Center. She got her PhD in HCI from Penn State and master/bachelor degrees from Tsinghua University.

Resources mentioned:

User-Centric Thinking in Data Science: Conversation with Anna Wu at Google Cloud (Part 2 of 3)

In this second part of my interview with Anna Wu, she describes the interconnections between data science and qualitative UX research.

Here is the first interview if you would like to start from scratch, and here is more information about Interview Series that this is a part of.

Here is Part 1 and Part 3 of our interview.

Anna Wu, established leader in building and leading high-performing data teams to drive changes impacting hundreds of millions of users. Currently as a research manager at Google, she leads a team of quantitative UX researchers applying UX methods and large scale analytics to inform Cloud product development. 

Before this recent chapter, Anna had 10+ years practicing UX and data science at top IT companies and research labs as a UX researcher, data scientist, research scientist at Microsoft, IBM Research and Palo Alto Research Center. She got her PhD in HCI from Penn State and master/bachelor degrees from Tsinghua University.

Resources mentioned:

Data Science and Game Design: Conversation with Clayton Sisson (Part 1 of 3)

Clayton Sisson is a game designer and aspiring data scientist, passionate about how data science can shed light on human behavior. For the next installment of my Interview Series, we discuss ways to use game design and UX design to develop usable and useful machine learning products and their experiences transitioning from design into data science. In this first part, we discuss the connections between data science and game design.

Here is Part 2 and Part 3 of our interview.

Resources:

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