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From Data Work to Data Science: Getting Past the Gatekeepers (ICER 2023) - Lara L. Schenck

From Data Work to Data Science: Getting Past the Gatekeepers (ICER 2023)

This post outlines my lightning talk and poster for the 2023 International Computing Education Research conference. It’s a nice update regarding where my research is headed and what I’m working on lately.

Image of the poster I am presenting at the ICER 2023 conference. See the PDF for a screen-reader friendly version here: https://notlaura.com/wp-content/uploads/2023/07/ICER-2023-LSchenck-Poster.pdf.

Welcome to the post summing up my very first presentation at an academic conference! This post contains links to more information and written versions of my 3-minute lightning talk and poster talk. I hope you find something interesting, and feel free to be in touch because I love meeting new people!

Table of Contents

    Lightning Talk (3 min)

    The slide for my Lightning Talk. Screen reader-friendly version in the PDF here.

    How many of you out there have a computer science degree?

    How many of you don’t have a computer science degree?

    For those of you who don’t, have you ever felt like there was some kind of secret language you didn’t understand, or that somehow you needed a computer science degree even though you were already great at your job?

    So, I don’t have a CS degree either, and know this pain. I’d like to start by sharing a story from my previous career in software development. I had been working as a developer for a few years, and I applied for a job titled UX Engineer/Interaction Designer. It sounded like the perfect fit for my mix of design and development skills. The interview started out really well with a conversation about all of the front-end topics I was really passionate about. Then came time for the technical interview in code editor over screenshare. The first question, “Have you heard of FizzBuzz?” … And I was like, “No, I haven’t… what is that?” Then the interview says, “Print the numbers from 1 to 100 and every third number print fizz, every fifth number print buzz”.. and so on. And here I’m feeling a little panicky, and I said something like, “I don’t understand what you are asking me to do. Is this something I would be asked to do at the job?” And the interviewer replies, “Oh, no, this is more to test your computational thinking.” And I’m thinking, “Test my what?”

    Needless to say, I did not get that job, but I did look up this “fizzbuzz”, which I quickly learned is an interview question to “filter out fake programmers”.

    Now, let’s unpack a little what happened here. I had been working as a developer for several years, and I applied to a job I thought I was qualified for, but the software development community didn’t think my skills didn’t count as “real” programming, and I was filtered out.

    Eventually, I did get a software engineering job, and now, through research, I am on a mission to make it easier for people without CS degrees to learn computing skills and grow computing careers through learning at work. I use the phrase “novice-friendly computational work” to describe work like the web design & development work I did early on in my career. In my research I partner with DataWorks at Georgia Tech to understand novice-friendly computational work in a different context: data work, like data entry and data cleaning, and how data work can be a on-ramp to data science careers.

    So, I hope that my story sparks conversation here at ICER, and I invite you to visit to my poster to learn more about DataWorks and about what workplace curriculum can do to support novice data workers getting past the gatekeepers. Thank you!

    Poster Talk (3 min)

    Image of the poster I am presenting at the ICER 2023 conference. See the PDF for a screen-reader friendly version.

    Hello! My name is Lara Schenck and I’m a second year PhD student at Georgia Tech. I am researching how data work can be a pathway to data science careers in partnership with DataWorks, a small data services provider housed at Georgia Tech. DataWorks hires and trains people from groups underrepresented in computing with little technical background for a one-year, full-time paid position completing data tasks like cleaning, annotation, and data entry for client projects.

    This kind of data work is an example of what I call novice-friendly computational work. Novice-friendly computational work is full-time work in computing that gatekeeping culture often considers separate from “official” or “real” computing jobs. Examples include data work, web design, and Salesforce administration. People can stay in these novice-friendly positions, but they can also be a springboard into other career growth in computing, for example, moving from web design to software engineering, or from data entry to data analysis. These pathways of learning through work are promising but they are difficult, and in computing education, we know little about how to support them.

    In this poster, I share my in-progress work investigating career development in workplace curriculum as a way to support novice data workers interested in pursuing careers in data science. Last year, I designed and delivered a career development curriculum integrated into DataWorks’ training curriculum, and I completed a pilot study to understand how workplace curriculum can support career growth in data science. I am currently analyzing data from a pilot study with five data workers and running the curriculum a second time. So far, I’ve found that a key consideration is that workplace curriculum address the difference in how to find a salaried job compared to hourly wage jobs, because for salaried job search, you can’t go out, apply to a job, and hear back in a week. There’s a lot more that’s hidden in this process, and even if someone has the technical skills, knowledge about the “game” you have to play is critical for landing a salaried role that could be the next step in a computing career. Here, you can see some quotes that outline examples of these differences, and here you can see details on the curriculum.

    I’m looking forward to hearing what folks at ICER think about this work and how you think it fits in, or doesn’t, in computing education research. I’m happy to answer any questions about this project or to share more about DataWorks or ideas I am considering for future work. Thank you!

    Ideas for Future Work

    Future work to understand how workplace curriculum can support data workers’ career growth in data science could approach the topic from a “studying up” perspective. This idea comes from anthropology and refers to research studying the people who hold power vs. the people who are experiencing oppression or marginalization. One idea is to research interviewing in organizations, e.g. how are interviewers trained, how to they decide what to ask in technical interviews and why. Also related is understanding what skills are really needed to perform the job, and how to interview for those.

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