Before Data Science: Statistics
This post is a companion to our previous post, Beyond Data Science: Industrial Engineering. The notion in that article was that there is more to analytics than just data science, the specific case being industrial engineering.
For a long time people made the distinction between data science and statistics, with the former related more to machine learning techniques, mostly developed in the computer science field. This is changing. Data science is increasingly considered to be inclusive of statistics.
As companies build their analytical chops it is important not to be too narrowly focused on data science and machine learning. The article on industrial engineers was entitled “Beyond Data Science.” This one is entitled “Before Data Science” There are two senses of the word “before”:
- Data analytics was considered to be the purview of statisticians before the rise of machine learning, which has an approach for many of the same issues, coming from the computer science community.
- Even for machine learning professionals it is increasingly recognized that a grounding in statistics and an understanding of probability distributions is necessary before becoming a trained data science professional.
Perhaps some perceive statistics to not be as glamourous as “the sexiest job of the 21st Century” But there are some very rewarding applications. Though we are a small company, First Analytics has a social responsibility philosophy. Our company culture statement contains this sentence: “we find gratification working on projects related to safety, human and animal health, energy conservation, and resource efficiency.” One of our team members recently enjoyed working on a project that was a bit different than usual. You can read about this evaluation of an opioid addiction treatment in this article, or summarized in this case study. This team member, a trained statistician, is also a practicing data scientist. His education and background in statistics has made him a better data scientist.
As was advocated in the post Beyond Data Science: Industrial Engineering, it is important for organizations who are building their “data science team” to look for a broad mix of skills and experiences, including those individuals with an appreciation for, or experience in statistics.