The Field of Data Science & Yet another New Data Science Venn-Diagram…
For a long time, I have struggled to understand the facets that are make up the Field of Data Science.
The depth of knowledge in “Data Science” is becoming large and methodologies so unique, it is irrational to lump it into another traditional field such as Mathematics, Economics, Computer Science, or Statistics, or to relegate its capacity to a Job Title. It is in it’s own right a separate field. A field that encompasses several different unique job titles, and one who’s community is heavily dedicated at collaborating and innovating unique new processes, like traditional researchers, but through Open-Source Collaboration. The only caveat to this end is that the rate of knowledge being generated is exponentially outpacing the capacity for universities to incorporate curriulum to bridge the gap between current understanding in the field and their antiquated and slow pedogogical systems.
In order to overcome this obstacle online courses, programs, and in-person bootcamps were created to speed the acceleration of competent data scientist in to the workforce. Even then to have the students realize that regardless of the companies stance that the Data Scientist postion doesn’t require a PhD, it does still however require an education beyond the eductaion that they could reasonably attain
My Data Science Journey
I was fortunate enough to have pursued an undergraduate degree in economic’s which opened the door for me to the world of statistic’s, probability theory, game theory, among other interesting quantitive theoretical practices. Beyond that my personal interest in software development, and just an overall curiosity for understanding why things happened, led me to Data Science. After graduation, rather than doing a bootcamp, or MOOC, I decided to take the risk and do an online Masters in Data Analytics. I have been extrodinarily underwhelmed by the courses which are primariliy are taught using Excel, and at most cover the extent of what multiple regression is and how to implement it.
On the plus side, simply having that master’s degree helped me to land my awesome job at a Fortune 1000 company, and utilize data science everyday. I am working as a Market Research Analyst, but behave more like a data scientist in my day-to-day functions. I use Python and R in all of my analysis projects, and use Excel as a repository and an improvised from of version control. I preform analysis on the Customer Analytics side of the company and try and derive meaning as to the sentiment and satisfaction from our customers, in order to help the business better communicate and connect with their current customers. I also act as the liason between the Big data team and work within the Hadoop Cluster writing SQL (i.e. HiveQL) to pull relavant information in for our team.
Most of you can intuitively piece together my job title is not accurate. I am not a Market Research Analyst, and I am not a Data Scientist.
But what am I?
A Decision Scientist.
After listening to the awesome podcast with Drew Conway and Hugo Bowne-Anderson, I listened to Drew Explain how he generated his extremely popular Data Science Venn-Diagram.
It was within this podcast that I found the inspiration to add on to the the work that Drew did and make a more inclusive diagram of the additional components in the field, today.
The Field of Data Science Venn-Diagram Explanations
The additional component which was missing in Drew’s inital version was what I call the..
Entrepreneurial Capacity: It is representitive of the capacity to think big enough that there can be a broad range of application in your ideation, and to have the ability to strategically implement the approaches necessary to achieve your desired outcome.
With it’s addition a more illuminating picture of the Field of Data Science can be made. The relevant additions include:
Decision Scientist: An individual who can draw on all four quadrants of the diagram to utilize data science to understand and interperate business problems from data, and who has the capacity to produce actionable data and non-data products. Decision Scientists help to convey meaning to organizations, and help them to consume and use analytics.
Data Scientist (Not an addition, but a re-classification): An individual who can draw on the Mathematical Skills, Hacking Skills, and the Substantive Experience quadrants of the diagram to utilize data science to understand the mathematical and statistical intricacies and significances in the data, and who have the capacity create pipelines for data creation, and collection alongside software engineers, and Database admins. Data Scientists help organizations create, generate, and process meaningful analytics.
Business Intelligence Developer: An individual who can draw on the Entrepreneurial Capacity, Hacking Skills, and the Substantive Experience quadrants of the diagram to utilize data science to understand the relevant information within the data, and to utilize less-mathematical and statistical implementations than that of a data scientist. Unlike the decision scientist, BI Developers tend to use more of an entrepreneurial capacity to develop meaningful interpretations of the data to generate meaningful Dashboards, and interactive visualizations. BI Developer is the intersection between a Software Developer and a Decision Scientist.
Data Analyst: An individual who can draw on the Mathematical Skills, Hacking Skills, and the Substantive Experience quadrants of the diagram to utilize data science to understand the relevant information within the data, and to utilize less-computational complexity than that of a data scientist. Data Analysts don’t necessarily need to have the ability to rival Data or Decsion Scientist in terms of their hacking skills yet they can develop relevant interpretations of the data to generate actionable insights, through Excel, Stata, SPSS, etc types of software. Data Analyst’s are an intersection between Decision Scientists and Data Scientists.
Conclusion
It is in my opnion that these extra components help to shed a more inclusive light onto the growing field of Data Science, and to at the very least strike up a conversation as to the actual placement for the new sub-classification within the data science field. Anyways I digress..
Keep it Logical,
-> Jeremy A. Seibert
