Data science & data analytics are both fields of study wherein data are used to solve problems. Both utilize [[Exploratory Data Analysis]]. I used the two terms almost interchangeably up until I began my Masters in Data ***Analytics*** and found myself saying "oh... so what am I not learning here?". The answer is, obviously, I don't know what I don't know. I do think I have an idea, though, that data science has more to do with *predictive* uses of data (statistics, machine learning, other do-the-math type things). Data Analytics has more of a *descriptive* bent to it. I learned some basics of how to use [[Machine Learning]] in grad school, but I never really learned what `scikit` was doing under the hood. I learned [[Jupyter Notebook]]s, but spent an equal amount of time with [[BI Tools]]. The two terms are overlapping. They both exist in the layers of my [[Data Tools Overview]] note: 1. Data Storage 2. Ingestion & Transformation 3. [[Exploratory Data Analysis]] 4. Modeling, [[Machine Learning]], and [[Statistics (index)|statistics]] ← MORE DATA SCIENCE-Y 5. Visualization & BI tools ← MORE DATA ANALYTICS-Y **** # More ## Source - Grad school + an enlightening chat with ChatGPT