Data science and Data Analytics : people working in the tech field or other related industries probably hear these terms all the time, often interchangeably. However, although they may sound similar, the terms are often quite different and have differing implications for business. Knowing how to use the terms correctly can have a large impact on how a business is run, especially as the amount of available data grows and becomes a greater part of our everyday lives.
Much like science is a large term that includes a number of specialities and emphases, data science is a broad term for a variety of models and methods to get information. Under the umbrella of data science is the scientific method, math, statistics, and other tools that are used to analyze and manipulate data. If it’s a tool or process done to data to analyze it or get some sort of information out of it, it likely falls under data science.
Practicing data science boils down to connecting information and data points to find connections that can be made useful for the business. Data science delves into the world of the unknown by trying to find new patterns and insights. Instead of checking a hypothesis, like what is usually done with data analytics, data science tries to build connections and plan for the future. Data science often moves an organization from inquiry to insights by providing new perspective into the data and how it is all connected that was previously not seen or known.
If data science is the house that hold the tools and methods, data analytics is a specific room in that house. It is related and similar to data science, but more specific and concentrated. Data analytics is generally more focused than data science because instead of just looking for connections between data, data analysts have a specific goal in minding that they are sorting through data to look for ways to support. Data analytics is often automated to provide insights in certain areas.