Data Science VS Data Analytics
August 8, 2019 2019-08-08 15:30Data Science VS Data Analytics
What Is the Difference?
While many people use the terms interchangeably, data science and big data analytics are unique fields, with the major difference being the scope.
Data science is an umbrella term for a group of fields that are used to mine large datasets. While, Data analytics is a more focused version of this and can even be considered part of the larger process. Analytics is devoted to realizing actionable insights that can be applied immediately based on existing queries.
Another significant difference between the two fields is a question of exploration. Data science isn’t concerned with answering specific queries, instead parsing through massive datasets in sometimes unstructured ways to expose insights.
Data analysis works better when it is focused, having questions in mind that need answers based on existing data. Data science produces broader insights that concentrate on which questions should be asked, while big data analytics emphasizes discovering answers to questions being asked.More importantly, data science is more concerned about asking questions than finding specific answers. The field is focused on establishing potential trends based on existing data, as well as realizing better ways to analyze and model data.
| Scope | Macro | Micro |
| Goal | To ask the right questions | To find actionable data |
| Major Fields | Machine learning, AI, search engine engineering, corporate analytics | Healthcare, gaming, travel, industries with immediate data needs |
| Using Big Data | Yes | Yes |
The two fields can be considered different sides of the same coin, and their functions are highly interconnected.
Data science lays important foundations and parses big datasets to create initial observations, future trends, and potential insights that can be important. This information by itself is useful for some fields, especially modeling, improving machine learning, and enhancing AI algorithms as it can improve how information is sorted and understood. However, data science asks important questions that we were unaware of before while providing little in the way of hard answers.
By adding data analytics into the mix, we can turn those things we know we don’t know into actionable insights with practical applications.
When thinking of these two disciplines, it’s important to forget about viewing them as data science vs, data analytics. Instead, we should see them as parts of a whole that are vital to understanding not just the information we have, but how to better analyze and review it.
