The following are all articles I’ve written on Medium grouped by topic.

Data Strategy Newsletter

I’ve started a Substack newsletter on data strategy for folks who work in the data space and those interested in learning more — even individuals who are curious if they can better use data in their day to day lives from entry level employee up through the C-suite. I’ve imported these posts to Medium to reach folks here as well. If this sounds interesting to you, click here to subscribe.

Welcome to the (Data) Jungle (also on Medium in CodeX): Introduction post to the newsletter on how data strategy is a complicated problem that organizations and individuals need to get better at. After solving the easy problems, things get a bit thornier. My newsletter is dedicated to investigating these complexities.

Why Do We Care About Data? (also on Medium in Geek Culture): I share a *one sentence* explanation of why organizations and individuals should care about data (and data products). Sound too good to be true? Prepare to be amazed!

Data of Decision, by Decisions, for Decisions (also on Medium in MLearning.ai): I examine what happens when data resources are deployed without decisions in mind and propose a framework to maximize the usefulness of data products by recognizing that:

  • (of) The data available is the result of prior decisions.
  • (by) Choosing the correct data product to inform a decision is a decision in itself, and should not be skipped.
  • (for) Data product results need to affect the decision of interest.

Concepts

I like to explain concepts starting with the basics and building up from there, which is also known as reasoning from first principles. Although it is harder and takes longer than the alternative, I find it extremely powerful and have written about its benefits. I typically write about topics I’m passionate about and eager to improve in: data science and decision intelligence.

Grappling with Event Probabilities Using Multiverse Theory (Towards Data Science): What does it really mean that a basketball team has an 89% chance of winning a game, or there’s a 10% chance of rain tomorrow? How do we interpret (and often criticize) these predictions if the basketball team loses and we end up getting rained on? I go over a more intuitive framework of dealing with event probabilities, especially when the improbable happens, by using multiverse theory.

Reasoning from First Principles: To reason from first principles, one must clearly state all definitions and assumptions underlying their thinking. I share the powers of this technique: it leads to deep understanding of the subject at hand, it avoids confusion and miscommunication, and surprisingly, can lead to creative solutions.

Anatomy of a decision (UX Collective): We make dozens of decisions every day, from snoozing our alarms instead of getting out of bed, picking which reality TV show to binge next, and even deciding whether to finish the Medium articles we’re currently reading. In this article I borrow from economics and game theory to talk about how to formally represent what it is we’re trying to accomplish when we make decisions.

Business Concepts for Data Scientists — Finance/Econ (Part I) (Data Driven Investor): Unless you’re working in a specialized area, to succeed in the field of data science you need some basic knowledge of business concepts. I started this series to help those new to data science and working in industry pick up these concepts and provide examples relevant to how data scientists may use these concepts. In Part I, I focus on Finance and Economics.

Business Concepts for Data Scientists — Marketing (Part II) (The Startup): Second part of the series. This one is on marketing concepts explained in ways that data scientists should find useful.

Business Concepts for Data Scientists — Subscriptions (Part III): Third part of the series. This one is on important concepts for subcsription businesses explained in ways that data scientists should find useful.

In the Office

I write about experiences I’ve had as a predictive modeler-turned manager of predictive modelers with communicating results, managing stakeholders, and leading complex projects. Some articles are data science specific and others deal with more generalizable experiences.

Welcome to the Data Team! Please Solve Everything. (Part I: The Problem) (Towards Data Science): One frequently hears that people and organizations aspire to be data driven, but data science can’t solve everything. Sometimes people, particularly those in power, ask their data analysts and scientists to accomplish tasks that they’re not well prepared for. I go over three categories of this and propose a role that involves responsible and intelligent use of data.

Welcome to the Data Team! Please Solve Everything. (Part II: The Solution) (Towards Data Science): I define and expand upon the hypothetical role of data superlibrarian (more commonly known as data strategist and data product manager). They learn about data needs that people in an organization have and direct them to appropriate internal data products or acquire external ones.

Welcome to the data team! Please solve everything. (Part III: The Solution Ignored) (Towards Data Science): If data strategists and product managers are so important, why don’t we see more of them? I share and expand upon three reasons: organizations don’t realize the role exists, they don’t think they need the role, or they (incorrectly) assume that their data science managers are already doing the role (well).

Writing

Doesn’t Medium have enough writers writing about writers writing? Yes. Am I willing to exacerbate that problem by sharing my own experiences with the hopes that they help at least one other person out there with their craft? Also yes.

How to Handicap Your Inner Critic to Get More Writing Done (The Startup): I’ve always struggled with getting words on the page when I write. Once, I stared at a piece of short fiction I was working on for 90 minutes before squeezing out a sentence. I’ve found this is due to my harsh inner critic, who is always saying things like “are you sure that’s the right word?” and “this sentence isn’t even remotely related to anything else you’re trying to do here.” I share my tips on circumventing this inner critic in this article by embracing one’s inner madman. Quieting your inner critic as you get different ideas on the page can lead to stronger final works.

Director of Quantitative Analysis @PandoraMusic. Mentor @Springboard. Bridging the gap between business and data teams. Opinions are my own. #rstats