What's the Difference Between a Data Scientist and a Data Analyst?
US Average Salary: $75,000 (Indeed)
Data analysts collect, clean and consolidate data before analyzing it for key insights that may be essential for strategic decision making. They will frequently create data visualizations to help stakeholders understand the information represented within the data. Data analysts frequently collaborate with company leadership, marketing, or finance teams to help them achieve a data driven decision making process. Overall, data analysts are focused on identifying trends within data to help businesses make better, data-driven decisions.
Data analysts usually have a Bachelor’s degree in mathematics or statistics, and some have further education.
US Average Salary: $122,000 (Indeed)
Harvard Business Review called being a data scientist the “sexiest job of the 21st century.” (HBR) Data Scientists use knowledge of computer science, math and statistics to collect and clean data, engineer databases and analyze data to find insights, including implementing machine learning models and other AI techniques.
At least a Bachelor’s degree in computer science, software engineering, statistics or mathematics. Many data scientists also have masters degrees in data science or one of the aforementioned fields.
So what's the difference?
The biggest difference between data analysts and data scientists is programming knowledge. Data analysts do not require much programming knowledge, whereas it is essential that data scientists have advanced programming skills. Data scientists can do most of the same things as data analysts, but focus their time on making models and predictions. Data scientists focus on the entirety of the data process from collection, cleaning and analysis to model implementation, while data analysts focus mostly on the end of the process, finding insights to answer business questions.