Learner Personas

A learner persona is a short description of a typical learner. By personalizing learners, personas help instructors figure out who they’re trying to help and communicate that understanding to each other and to learners. Each persona has general background, relevant prior knowledge or experience, the learner’s perception of their needs, and any special considerations.

Any workshop, tutorial, how-to guide, or reference manual should be designed for one or two specific personas. The ones below are derived from a set originally developed by the author for RStudio.

Notes

1. A novice is someone who doesn’t yet have a mental model of some field: as a result, they don’t know what they don’t know. Someone is competent if they know enough to perform routine tasks without heroic effort, while they are an expert if they are able to solve common problems at a glance and harder or more unusual problems reliably.

2. The domains in these personas (such as pharmacy or event management) are meant to be placeholders: swap in another domain such as finance or logistics as desired.

Anya Academic

1. Anya, a professor of neuropsychology, studies color perception in infants. She is also responsible for teaching an introduction to statistics to 1100 first-year students every year. (Students complain that the Stats department’s introductory course is too theoretical and requires more programming knowledge than they have.)

2. Anya runs several experiments on 50–100 infant subjects each year. She used to analyze the results with R, but is switching to Python (which she taught herself during a sabbatical). She has never taken a programming course, and suffers from impostor syndrome in discussions about things like GitHub.

3. Anya would like to learn more about tools like Polars and Marimo. She also wants guidance on using them to teach her intro stats course, which currently uses R.

4. Anya is juggling half a dozen responsibilities at work, and has been burned before by investing in technologies that didn’t pan out.

Anya needs workshops (so that she can allocate focused time) and how-to guides (for her research). She would like ready-to-use lesson material she could remix for her students and some orientation material to demystify jargon (what the hell is a “pull request”?). Finally, it’s important that she be able to use the same tools in her research as in her teaching in order to amortize learning costs.

Celine Certified

1. Celine has an MBA in finance and is now a certified professional instructor working for a full service solution provider. She spends her time developing new training material in airports and delivering them between flights.

2. Python is the only language Celine has ever learned well, but she has learned it very well. She is proficient with a variety of packages for modeling and time series analysis, and knows more than any sane person should about extracting data from Excel. She regularly contributes small fixes to a dozen of the packages she teaches most often.

3. Celine is always interested in seeing other people’s teaching material, but what she appreciates most is up-to-date examples she can recycle and build her own lessons around.

4. Celine is sometimes frustrated by how little developers think about the learnability or usability of their packages, and wishes she had more control over what she’s going to be asked to teach.

Celine needs vignettes, worked examples, and cookbooks that she can mine to create training to meet her audience’s needs. She would also like pointers to material on better teaching practices.

Exton Excel

1. Exton taught business at a community college for several years, and now does community management for an event management company. He still teaches Marketing 101 every year to help people with backgrounds like his.

2. Exton uses Excel to keep track of who is registered for webinars, workshops, and training sessions. He doesn’t think of himself as a programmer, but spends hours creating complicated lookup tables to figure out how many webinar attendees turn into community contributors, who answers forum posts most frequently, and so on.

3. Exton knows there are better ways to do what he’s doing, but feels overwhelmed by the blog posts and “helpful” recommendations from the company’s engineering team.

4. Exton is a single parent; the one evening a week he spends teaching is the only out-of-work time he’s able to take away from family responsibilities.

Exton wants an overview that will tell him what laptop-scale data science is all about, what tools to learn first, how they’re going to help him, and where he should look for introductory tutorials. He doesn’t care if these are the best answers so long as they are clear, concise, and consistent. He would also benefit from side-by-side comparisons with Excel.

Jacqui Ofalltrades

1. Jacqui works for a three-person consulting company that does everything from training to installation and administration to building dashboards for clients. She is fluent in Spanish and Portuguese as well as English, and much of her business is with Latin American firms.

2. Jacqui has been using Python and Pandas for several years, and just finished working through some material on PyTorch for the second time in preparation for teaching it. She has built a couple of Dash dashboards for clients.

3. Jacqui wants updates on new data science packages and tools, and advanced guides for making dashboards faster, integrated with no-SQL databases, and other high-value topics. She finds most self-paced tutorials frustrating because they’re answering questions she doesn’t have today.

4. Jacqui believes that time is money: every minute she spends learning something new has to pay off sooner rather than later.

Jacqui wants how-to guides and reference material for her day-to-day work, webinars to give her a sense of where the industry is going, and short, intensive online training for very specific topics.

Katrin Keener

1. Katrin did a Master’s in neuropsychology and a 12-week data science bootcamp before getting a job analyzing logistics for a health care services company. She has a wide range of interests and loves learning new things.

2. Katrin uses Python and SQL daily, and recently talked one of the IT staff into teaching her Docker. She has done a couple of online short courses in machine learning.

3. Katrin wants to level up her understanding of just about everything. She needs cookbooks and worked examples to show her how to accomplish specific tasks or to fill in specific gaps in her knowledge.

4. While she is enjoying her new job, Katrin misses the camaraderie of grad school and her bootcamp: she prefers learning with others to studying on her own.

Katrin wants workshops (as an opportunity to meet people) and self-paced lessons (so that she can learn new things on her own). She has a complete set of cheatsheets taped up on her wall, but usually asks Claude before looking at reference manuals.

Larry Legacy

1. Larry got a job with a pharmaceutical company the day after he graduated and has been with them through two mergers and an acquisition. He refers to company officers by their first names, and is always happy to explain what they’re doing wrong.

2. Larry has been using Vim and NumPy for 25 years and regards them as a perfectly fine tools, thank you very much.

3. Larry has never used a computational notebook, but has decades of experience with statistics, messy data, and reporting. He prefers very structured learning environments and clear objectives, and his most common question, “How would I…” followed by a summary of something he has been doing for years in NumPy.

4. Larry is a reluctant learner: he recognizes that he has to learn new tools now that management has decided to adopt Marimo, but with just eight years to go until early retirement, the thought makes him weary. He is very uncomfortable with anything outside his normal working environment.

Larry finds tutorials long-winded (“Just show me what function I need to call!”) or confusing (“Why would they do it that way?”). Cheatsheets showing him how to map his understanding to equivalent steps or code in other tools would be his preferred starting point.

M’shelle Manager

1. A programmer who moved into customer support and then into product management, M’shelle is now in charge of a ten-person analytics group. She is responsible for getting her team trained and purchasing the tools they need to do their work.

2. M’shelle used Python to develop statistical models and generate reports several years ago, but recognizes that she has fallen behind developments since then.

3. M’shelle wants to improve her team’s processes, particularly around packaging code and making work more reproducible. The new VP of engineering has also asked her to turn everything into a web service and get everyone to vibe code. While all of this is going on, she needs to decide how much budget she needs for new software that she will personally never use.

4. M’shelle has to work within a strict training budget, and it can sometimes take months to get approval to do anything.

M’shelle needs tutorials, reference material, and how-to guides for everything that isn’t data science, including testing, packaging, and how to use AI. (Her team is satisfied with the existing tutorials on data manipulation and modeling.)

Nang Newbie

1. Nang is 18 years old and in the first year of an undergraduate degree in urban planning. He’s read lots of gushing articles about AI, and was excited by the prospect of learning how to do it, but dropped his CS 101 course after six weeks because nothing made sense. He’s doing better in Anya Academic’s course (which he is taking as an elective), but still spends most of his time prompting and swearing.

2. Nang did well in his high school math classes, and built himself a home page with HTML and CSS in a weekend workshop in grade 11. He has accounts on nine different social media site, and attends all of his morning classes online.

3. Nang wants self-paced tutorials with practice exercises, plus forums where he can ask for help.

4. Nang is reluctant to reveal his ignorance: he would rather get a low grade and blame it on partying than let his classmates see that he’s floundering.

Nang needs to build enough understanding of data science to drive LLM tools effectively. He prefers short overviews to orient him and introductory tutorials that include videos or animated GIFs showing exactly how to drive the tools, and that use datasets he can relate to.

Toshi Techsupport

1. Toshi does internal tech support in a 500-person company. While others on his team take care of resetting passwords, he debugs setup issues and figures out why the dashboard is displaying nonsense. (It’s usually something to do with date formatting.) He often winds up writing bits of code from Stack Overflow to glue things together.

2. Toshi speaks Bash and Python, but only knows a bit of JavaScript. He switches back and forth between Linux, Windows, and Mac every day. He often finds himself running hour-long internal training seminars, and would now like to learn some data science to support his users and out of personal interest. He wishes people would take a few hours and learn more about the software they’re using, but in practice, he often only has a 30-minute call in which to diagnose the problem and explain a solution.

3. A growing number of analysts inside Toshi’s company use Marimo to prepare reports. He would like to learn enough about them to be useful.

4. Toshi lives in Hawaii, so most of his work is done remotely.

Toshi needs examples and reference material for himself that he can paraphrase for the people he is supporting, or that they can feed to AI tools. He also needs tutorials he can remix for hour-long internal training webinars.