As discussed in many of the articles below, defining what makes a data scientist seems to be quite complicated. I’ve always thought of them as being rebranded statisticians, but with a more current name. However, I’m learning that there are some skillset and responsibility differences that set data scientists apart from their fellow statisticians. Being able to handle data with thousands of variables and millions of lines of code is one skill that data scientists must master and that statisticians can avoid. Presumably, being able to manage that much data requires superior coding skills in multiple languages and maybe some knowledge of artificial intelligence. Data science seems to be about taking massive amounts of data and using statistics and dynamic code to learn from it as efficiently as possible. Data scientists are who drive the world’s most impactful business decisions for companies like Google and Facebook, while the word “statistician” evokes images of smaller clinical trials and carefully planned experiments or surveys. Data scientists know how to make sense of data sets that never stop growing.

Although the traditional role of statisticians may be based more in statistical theory than in programming, I think the profession is evolving to meet the current big-data-driven world’s needs. Statisticians still have a massive knowledge base of statistics and mathematics, but many educational programs are shifting toward more of a focus on programming so that their graduating statisticians have a better chance of qualifying for those data science jobs. More classes about artificial intelligence and developing strong programming skills are popping up in course catalogs to bolster theory-based statisticians with the computer skills needed to use their statistics knowledge efficiently and on a large scale.

As for myself, I believe I’m a statistician who is gaining the skills necessary to enter the world of data science. Most of my education has been about statistical theory and practice, but I’ve taken more recent classes that have allowed me to gain a strong knowledge base in SAS, dip my toes into SQL, and dive deep into R. I think it’s honing these skills, as well as exploring deep learning and artificial intelligence, that will prepare me to walk the common ground between statistics and data science.

~ Autumn

https://medium.com/odscjournal/data-scientists-versus-statisticians-8ea146b7a47f https://www.springboard.com/blog/ai-machine-learning/machine-learning-engineer-vs-data-scientist/ https://www.simplilearn.com/data-science-vs-data-analytics-vs-machine-learning-article https://mixpanel.com/blog/this-is-the-difference-between-statistics-and-data-science/