Analyst/Scientist

Question

What is the difference between being a data scientist versus a data analyst?

Answer 1

Data Analyst: Data Analyst works to interpret data to get actionable insights for the company. With a strong background in statistics and the ability to convert data from a raw form to a different format (data munging), the Data Analyst collects, processes and applies statistical algorithms to structured data.
Responsibilities: Data collection and processing, programming, machine learning, data munging, data visualization, applying statistical analysis
Languages: R, Python, SQL, NOSQL, HTML, Java Script, C/C++
Data Scientist: A Data Scientist’s mission is similar to that of a Data Analyst’s: find actionable insights that are key to a company’s growth and decision-making. However, a Data Scientist role is needed when a company’s data volume and velocity exceeds a certain level that requires more robust skills for sorting through a rolling sea of unstructured data (big data) to identify questions and pull out critical information. The person then cleanses the data for proper analysis and creates new algorithms to run queries that relate data from disparate sources.
The Data Analyst typically runs queries against new data to find trends important for the organization and to help prepare data for the Data Scientists. Data Analysts are typically very good at SQL as well as being knowledgable of the core metrics an organization deems important. They can also write scripts and produce intuitive visuals.
The Data Scientist is primarily tasked with building models using machine learning. These models are expected to engender an organization’s software with product features that predict and explain; making applications adaptive. The quality of a Data Scientist’s models depends directly on how well they understand and prepare data, thus they will work with the Data Analyst when it comes to understanding and preparing data to build better models.

Answer 2

Thank you!!