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⏳ Questions Students Ask

Outside of my normal 9-to-5 job, I also teach a class at my alma mater of the University of Washington. My class tends towards people who are early or mid career and are attempting to either pivot into the field of data science, or become more skilled/literate with data analysis and use that to level up there already extant subject-matter expertise.

Having taught in the UW iSchool for a few years, I often get similar questions from my students in the classroom. I've captured some of those questions below and expanded on them more than I normally do in class.

💼 What do you look for when hiring? What do you recommend for building your first data portfolio pieces?

I have mostly done hiring at smaller organizations, so it tends to be higher-touch experience than at a large tech company, and you can use that to your advantage. In scenarios like this, if you can craft your portfolio examples tailored specifically for the role you're applying to, it really goes a long way.

For example, when trying to get into my first job, out of grad school I did a very niche environmental science data visualization for folks and that demonstrated that--while I didn't have deep expertise in the domain--I could quickly become conversant in new ideas that I would be introduced to.

In general, there are a handful of core things I look for in someone's portfolio when hiring:

  1. End-to-end work demonstrating skills across data science roles. An example of this is going from research question/business case -> implementation in code/infra -> analytical work -> output summary, which may or may not include visualization.

  2. A project/domain of interest or curiosity that hasn't been overdone. This shows you're a creative thinker and can approach problems from different vantage points, plus the subject matter is a differentiator unto itself. For example, instead of doing the typical Titanic random forest or iris k-means, do a data analysis project with the Great British Bake-Off or RuPaul's Drag Race.

  3. Different levels of information accessibility to show communication and collaboration skills. Maybe you summarize everything into a nice blog post and walk through your analytical process, but then you share your source on GitHub in a well-documented manner.

The best candidates have at least 2 examples like this on their personal site, but possibly more. Typically I weight an ongoing programming or data analysis project higher than I would a one-off, because it demonstrates consistent engagement with an idea over time, but I don't discount anybody for having tons of small projects.

🔎 How do you recommend tailoring your job search for the first time, especially coming from a discipline that is not focused on technology?

  1. Be kind to yourself. - You’re trying to switch careers into a new domain and also learn the technical aspects of the domain and also learn the new professional communication tactics, rhythms, and customs. Each of these things separately could be a full time job. It will absolutely take time to get you there. It’s really helpful to have conversations with other people who are doing similar work, support each other, and keep in touch about learning and growing. This honest, engaged human connection on the job search is powerful and like rocket fuel in some situations, and is what real networking is.
  2. Job hunting is like writing--just sit in the chair and do it. Block off a fixed window of time every day to look for jobs. Use affinity organizations (communities based around software you use in this domain, professional groups, etc. ), LinkedIn, and job boards to find jobs. Apply on the company website if possible, rather than through an aggregator. Save those jobs in some form (text doc or spreadsheet on your computer is okay), review them, and apply. If applying via Linkedin, bias toward companies where you have a 2nd or 3rd degree contact, and try to connect with them and ask for a referral. If you can make friends with a trustworthy recruiter, interview them for what they’re looking for. Use the context across all of these to find more jobs. Set some goal for yourself—personally, I keep going until I find five jobs I’m confident about (>60% responsibility, duties, or skill match) and apply to each of them.
  3. Learn to notice. Part of the work you have to do right now is understanding how filtering works to determine good jobs for you vs. bad ones. At this point, avoid the term “senior” and optimize for “junior” or jobs without a qualification at the beginning (senior may have management duties and would be less focused on technical expertise, but would still require domain knowledge, so you probably wouldn’t be well-suited). Read the descriptions closely and understand what what the firm is looking for as best you can—keep track of words and noun phrases that reoccur and try to do research into them if they’re unfamiliar.
  4. Build in reps as fast as you can. Try to get your first conversation with a hiring manager or recruiter as early in this process as possible. Ask for feedback when you can (some orgs like Amazon don’t give feedback). Some recruiters, being time poor, will end a conversation with you early if you’re not a good fit and tell you why, which is definitely to your advantage as rejections go. All the information you can gather here is a scientific experiment—what are the different things you can say to represent yourself or convey your experience to get a step further through the job-seeking process? Build in psychological distance so you can stay true to point 1! Half the battle is positioning; the other half is doing the work. You will fail, but you want to fail fast and fail often so you can learn faster & reposition.
  5. Augment your supplemental materials. While they won’t explicitly get you a job, a portfolio, reel, or showcase of your work is invaluable if you’re going to attempt to transition into the space from a field you don’t have professional experience in. If you can do an end-to-end project which captures use of your skills in some way, that will both level you up re: domain knowledge (for example, something my peers in school did was salad taxonomy -> ML classifier; based on salad metadata, assign category -> explanation of results) but also act as an artifact that supports your candidacy. It’ll give you something to talk about.

📕 How do I find the best resources to continue learning or professional development outside the classroom?

When I worked at the University of North Carolina helping faculty members with course design, one professor did something I liked a lot. She presented the fundamental elements of each unit to students as required readings, and then paired them with a section called "If You Want to Know More". In this section, there were materials designed to augment the narrative threads that often came up in class or in the other reading materials.

Programming, Computational Thinking, and Systems Thinking

Across the board I highly recommend books by Allen B. Downey. His materials were a major force multiplier in my ability to learn and understand programming, after coming from a non-STEM background.

Data Visualization, Narrative, and Communication

There's no avoiding the work of the greatest. Anything by Tufte is a great primer on visualization, but I think it's best to start at the start with The Visual Display of Quantitative Information by Edward R.Tufte.