Looking back on my previous posts, not much has changed about my view on what data scientists do. As I’ve grown in my skillset and knowledge of different data-handling techniques, ways to access data, as well as how to create beautiful visualizations and presentations, I’ve been discovering what it FEELS like to be a data scientist. Reading about the difference between a statistician and a data scientist is one thing, but using new tools and learning to THINK like a data scientist is another thing. I’m excited to keep using the tools I’ve learned and continue exploring this growing field.

Automation, Predictive Modeling, and Partners? Oh my!

Today I finished my first project in RStudio. I built a GitHub page through R that teaches visitors how to access an API, building user-friendly functions in the process, as well as performing exploratory data analysis.

My (not so hot) take on R

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.