Everyone is learning. Some are further ahead in the process.
Nearly 2,000 hours later, and I still don’t consider myself a teacher. Rather, to me, “teaching” is a lively and constant show-and-tell of the unending potential data science unlocks.
That’s why I struggle to say goodbye to being a full-time educator.
I’ve been fortunate to be a part of the instructional staff for General Assembly’s Data Science Immersive for a little over a year, which is better measured by four 12-week, intense cohorts. While I started as a lowly night-time TA, an opportunity came for GA to trust me to lead the program. I’m incredibly proud of our team: we transformed our operation to become one of the company’s best (and still hold an internal record for student satisfaction!).
But I’m even more proud of the students and co-instructors with whom I worked. Understanding GA’s model is requisite to empathize with and celebrate them. In exchange for a new career as a data scientist, GA asks that individuals devote themselves entirely to learning data science. Students hail from all backgrounds — college new grads, PhDs in Chemical Engineering, Ivy League degrees, and even those without bachelor’s degrees — and quit their full-time job to change their career in the span of a three-month window, thereby often foregoing both stable income and tremendous amounts of leisure time.
Instructors are, thus, placed at the nexus of one’s future aspirations. Anticipating a career transformation, students place their full trust in those at the front of the classroom. It is these stakes that drove me to give my students my absolute best self. (In fact, I never took a vacation day in my time here, even when I had time between cohorts, as I found there to always be something I could do to better serve learners. I should emphasize that was an entirely voluntary choice, and perhaps to GA’s dismay. I regret nothing.)
Given the uniqueness of the position, I learned a number of valuable ideals, all of which are described to be applicable for leaders well beyond teaching and data science. Sharing a few of them here is actually one way by which I plan to have a useful log for my future self.
Audience = Accountability
(You Don’t Know It Till you Teach It)
You know something. But do you know it well enough to explain it to a complete novice? What about a seasoned professional in the field? And both of them at the same time for two hours in which more detail may be required on any minutia? Now add an uncompromising deadline of a week-away 10:00 AM lecture.
Teaching forced me to take topics with which I was comfortable and humble myself to realize I only know them with, say, 80 percent completeness. When faced with an eager class (whose career depends on clear delivery), I had a built-in accountability mechanism to go the extra mile to guarantee understanding. For instructors, it is estimated every in class hour takes three to four out of class hours or prep, even for proficient topics, to assure completeness in a lesson. When was the last time you treated all your work with four times the level of scrutiny and swiftness to improve?
Creating accountability mechanisms — whether via peers or honest self-deadlines — is requisite to sustained high performance.
It’s All About Slope, Not Y-Intercept
I repeatedly told my students that I placed little emphasis on their educational background’s subject area and instead cared about their propensity to want to learn more in this moment. Even if a student has a PhD in Physics, their ability to succeed in an uncomfortable, fast-paced environment using Python for machine learning depends on their desire to want to learn and grow more. Thus, a student’s willingness to want to learn (slope of new knowledge intake) is dramatically more important than their prior education (y-intercept). (Of course, prior education may be indicative of one’s willingness to learn.)
This is not unique to students learning support vector machines one hour and the fundamentals of natural language processing the next hour.
Everyone should strive to surround themselves with others that value their willingness to improve higher than what they already know, starting with oneself.
As I saw Sam Altman say, hire for potential, not prior experience.
Be Wikipedia, Not (Only) arXiv
Teaching is the practice of effectively relaying information that is already known. Research is the process of uncovering new information. This is a critical difference. Teachers do need not to have been the ones to discover new information to be best at their job. Rather, it is critical that I could be the Wikipedia of all class topics: provide a detailed introduction with the ability to go into similarly approachable depths for all encompassing explanation (linked Wikipedia pages) and provide the option for research-based citation when extreme depth is necessary. I am the approachable, clear amalgamation of information, even if not its origin.
Contrast this with arXiv, where new research is published. Reading and interpreting papers is important, but only insofar that I can index and share the information effectively. In this manner, I can be a central resource for student queries that continues to provide valuable understanding.
This is true for non-teachers. Imagine a manager consistently comes to you for ideas on improving the business and subsequently takes credit. As that manager becomes promoted for your work, you equally become indispensable in that manager’s workflow as he/she relies on your insight. You become the Wikipedia of the situation: you are the initial point of concisely offered knowledge. Even if you do not have the depth requisite on every query, if you can point in the right direction for more information and cite your process, you become the first stop for all future questions. As you continue to build relationships with others in your firm and reflect humility, it will become clear to others that value lies with you rather than your manager. (James Altucher discusses handling situations like this in great depth.)
Building relationships and sharing your own wisdom, even if you are unaccredited, will yield better long-term results than attempting to suppress useful information.
I prioritized curriculum improvement for our students. Our initial curriculum rollout required a hefty amount of retooling, which provided an exceptional opportunity to learn and relearn a wide array of topics. This culminated in one of my happiest achievements at GA. The second version of curriculum merged San Francisco’s iteration with D.C.’s, thereby making hundreds of hours of materials our team wrote become the global default. Students from Hong Kong to Seattle (and the long way around the earth!) are able to learn from curriculum we produced. GA even internally recognized this, awarding a quarterly “GAccolade” as a top three performer across our 600-person operation in December 2016. (I still suspect they got the wrong Joseph, but shh.)
A tweet from Coursera Co-Founder Andrew Ng summarizes the need to learn quite well:
Trust and Devolve
When I was tasked with leading our Data Science Immersive program, I knew I needed help. I conned my good friend and Statistics M.S. Matt Brems into joining the instructional team. Matt equally has a passion for breaking down challenging advanced mathematics, statistics, and programming into digestible bits. He’ll be leading instruction in my absence.
But attracting Matt to the team was only the start. Encouraging and retaining top talent relies on entirely different principles. Ambitious, high performing individuals want nothing more than the autonomy to prove the value of their work yet proper recognition for their successes. When it came to scoping weeks and curriculum, I knew I could devolve total responsibility over specific lecture areas and be outdone by the result. I would make sure Matt knew I was impressed, and I would promote his work to our GA global counterparts to assure recognition was granted beyond myself. This had a positive feedback loop: I could set the tone for diligence, devolve a task, be impressed by the result, and assure recognition reinforced the cycle to continue.
Ultimately, you should seek to work with individuals with whom you can entirely trust full devolution and be surprised by the result — not just because of how good it is, but because you, yourself, would not have considered doing it that extraordinary way.
While this list is decidedly non-comprehensive of all I’ve been fortunate to learn from those around me, it is a good start at distilling general advice I would provide my previous and future self.
I owe an enormous thanks to many, but here I’d like to emphasize my gratitude to my four student cohorts, ranging from 12 students (DSI2) to 24 (DSI4). You all took an ineffable leap of faith to change your career in three months, and that has pushed me to do some of my best work. The effort you put in is symbiotic and reciprocated. Seeing your in-class for loop successes all the way to coding neural network chatbots on the job or deploying new T-SNE research methods has provided for exceptional meaning in the work I was able to do with the Data Science Immersive. I dedicated my career to improving yours, and you have over-delivered. Thank you.