Must Love Data Mondays: Are You a Type A or Type B Data Scientist?

I haven’t yet finished my Machine Learning course Week 2’s assignment on how to predict housing prices.  I’m planning on doing that tonight after the kids go to bed.  I’ll write about the assignment after I complete it. Instead of not writing anything, I’ve decided to write about a good article I read yesterday about a Data Scientists first two years at Twitter: Doing Data Science at Twitter: A reflection of my two year Journey so far. Sample size N = 1.

Great article about a Data Scientists first two years at Twitter and what they’ve learned from the experience. He writes about his four main responsibilities: Product Insights, Data Pipeline, Experimentation (A/B Testing), and Modeling. At the end of the article the author lists a few sources that helped him when he was starting out. It’s a must read for anyone interested in working in the field.

The most interesting part of the article was where he discussed the two different types of dat scientists: Type A or Type B. The A stands for Analysis. This type is interested in working with data to create information. It’s more of a statistician or business analyst. These types of scientists spend most of their times analyzing data to find answers rather than spending the majority of their time building large data pipelines. They are very good at statistics but can also know how to work with data: finding, cleaning, visualizing, and writing about data.

The B stands for Build. These types of Data Scientists are very strong coders who could have originally been software engineers. They understand statistics and how to work with large data sets to infer information but they are not statisticians. They focus the majority of their time build large systems and creating code to lay down data pipelines to find more information.

I’m currently more of a type A of data scientist at the moment. I’m good at the analysis portion of data science. It’s what most closely reflects my work experience and my current role. I can code but I’m not a software engineer. I’m working everyday to improve my programming because I do enjoy building but I don’t feel like I’m at that level yet. My main goal is that I enjoy working with the ever-increasing amount of data to turn it into information for my customers.

I feel every analyst is going to need to be able to know how to handle large amounts of data, work with statistics, and be able to understand a little computer code if they want to succeed in the coming years.

Other Interesting Articles I Read Last Week:

Learning to learn, or the advent of augmented data scientists

How Spotify’s Discover Weekly cracked human curation at internet scale