datafication What is Datafication ? Datafication refers to the process of transforming various aspects of the world into digital data. It involves… More on reddit.com

Just statistics turned engineering More on reddit.com

Uses advanced tools/processes in the intersection of statistics and computing science to create products and services that informs better decision making or automates that decision making away. More on reddit.com

Drinking coffee, checking reddit and stackoverflow, being in meetings. Joking aside, there is a flaw with your question, and that is that you assume that all data scientists do the same tasks, or have even moderately similar day-to-days. Put differently: you just asked the equivalent of "what do lawyers actually do in their day to day?" I imagine that your day to day looks very different if you're a litigator vs. an IP attorney, vs. an international tax law attorney vs. a forensic attorney vs. a constitutional law attorney. The same is true for data science, but with maybe even looser boundaries. To oversimplify the world, I would say there are going to be 4 types of tasks that data scientists do on some regular cadence: Research: you will have to read up on different ways to solve problems, or different tools/technologies that you can use, or how to tackle specific modeling issues, or how to call a function, etc. It can be as quick as a 5 minute read on a new package in Python, or as long as several weeks to do a comprehensive literature review on modeling methods. Code: once you somewhat know what you have to do, you have do it. Normally you will start by identifying the data that you will need, scoping it, examining it, cleaning it, looking at it some more, do some basic analysis on it, clean it some more, do more advanced analysis, clean it again, put it in a nice format for modeling, more cleaning, and then code up some type of model. Then you clean the data some more, tune your model, debug it, clean, tune, debug, debug debug debug debug debug debug, look at results, they don't make sense, debug debug debug debug debug debug, hey that looks like something that makes sense, oh wait, no, debug debug debug, ok, that looks reasonable. Communicate results: you now have results and you need to convince someone in the organization that those results are good, and that those results are useful. Discuss how to make data science work usable by the organization: once you are able to convince key people that your work is useful, you will need to work with other people across the organization to execute your work in a way that actually drives better outputs. Your usual suspects will be your enterprise development team, a project manager/management team, and the lead business unit responsible for the process that you are working on improving. More on reddit.com