1. Day in the Life of a Data Scientist
This story is based on the day-to-day of an industry expert in the financial sector, who wishes to remain anonymous.
Data scientists in finance try to predict whether or not people will default on their credit due to certain predictive factors or they help classify which transactions seem fraudulent. All of this requires a look at millions of lines of data, and it involves extrapolation to the future, a skillset almost all human beings are notoriously bad at. All of this requires a closer look at the data. However, the day-today isn’t just spent looking through numbers.
9 am
There’s a lot of legwork that goes into data science just like any other job. Nearly an hour is spent just catching up on email and organizing for the day ahead.
10 am
A surprisingly high amount of time in data science is spent recruiting. Demand for data science skills is at an all-time high, so data science organizations are often evaluating potential recruits. Data scientists will often take time out of their days to do phone screens of potential new team members.
11 am
Data scientists spend a lot of time in meetings. Almost an hour is spent just making sure that every team is properly aligned with one another, and working on the right things.
12 pm
Lunch offers the chance to relax a bit and catch up with colleagues. Then it’s back to the grind. One half of the typical day is spent coding an analysis or looking over somebody else’s code. This might involve building a graph to represent insights unearthed during a look through the data, or it might just be about making sure your own code is clean so everybody on your team can read through it and understand what is going on.
4 pm
Data scientists will often discuss with groups of fellow data scientists ways that they can collaborate and help one another. They’ll often learn together and share the latest tool that can help improve productivity.
2. Infusing Data in Your Workplace: Chase Lehrman
Chase Lehrman works as a data analyst at a fast-growing education company called Higher Learning Technologies that helps dental and nursing students pass their board exams. He describes his day-to-day as being a data storyteller who looks to gain an understanding of how users are using the product Higher Learning
Technologies sells. He also helps people across the organization get the data they need to make informed decisions: a recent example involved sizing a market.
Thanks to Chase, Higher Learning Technologies can change its static data into usable insights, something every data scientist should get their organization to embrace. Chase makes sure that data problems are framed the right way and that solutions are properly communicated and actionable.
Chase makes sure that data problems are framed the right way.
Data scientists solve many different problems. A data scientist might hunt for raw data. They might be asked to create automated programs that can process data quickly and efficiently. They might be asked to communicate their results and why they matter to the CEO of a company. You will have to learn a versatile skillset, and a variety of tools if you want to become one.
3 Understanding the Data: Sneha Runwal
Sneha Runwal works as a statistician at Apple, where she works in the AppleCare division. Her major work there involves forecasting and time series analysis, in addition to anomaly detection.
She feels that people are often too quick to delve into algorithms and computer code, but it’s important to step back and understand your data before you get into implementation mode. She says she is trying to get more disciplined about this herself. Her advice? Understand as much of your data as possible, as early as you can.