We – Gary Davis & Adriano Marzullo – are writing a series of articles on moving into data science.
Our aim is to demystify the process of become a data analyst and data scientist for people who are curious as to what this entails and thinking this might be a career option.
The first article in the series will be : What do data analysts and data scientists spend their time doing?
We want data scientists to share some of your experiences of daily work, particularly to give a prospective beginner some idea of what the day to day life of a data scientist entails.
We find it’s one thing to give a list of activities in which data scientists engage regularly – as in this article, for example – and quite another to hear in a practitioner’s own words, what their work life involves.
A couple of paragraphs outlining the most common activities of your work life, posted in the comments, would be really helpful.
We will, of course, acknowledge your contribution, and share the article with you when it’s written.
Here are a couple of helpful examples:
“My job is actually really nice in that we have some data standardization in place, so most of my job is modeling or automating parts of the pipeline (engineering). The goal is to have a completed project within a year that can be run by anyone in the company, not just the data science team. Then we can move on to new challenges.” (from Reddit Data Science)
“I have never self-identified as a data scientist, but I probably represent a notable percentage of the readership (of the LinkedIn Data Science Central Group). I am responsible for data pertaining to operations. I obtain this data from different sources: integrated systems, clients, and agents. Usually this data in its raw form cannot be used. So I spend time converting data and maintaining it in an accessible form. At times frequently and without warning, I have to establish patterns and trends relating to certain aspects of operations in an effort to gain future visibility. The emphasis is speed. It is necessary to deliver insights quickly. I have to overcome data compatibility problems right away. I need to have all sorts of reliable techniques and useful experiences. I created most of the logistics and process that I use. I try to make time to further its development. To date I haven’t found a more rewarding job since I tackle actual business challenges.” (from LinkedIn Data Science Central group)