The Difference Between Data Science and Data Analytics
Across multiple industries in the world, companies are utilizing big data as a way to gain more insight into how their business operates and how it can be improved. However, business executives often question whether or not data science is just the same as data analytics.
Data science has a primary focus on big data. Big data can be described as a volume of raw and unstructured data that is from a number of diverse sources. Due to its high volume, it requires more power to not only gather but to analyze. Big data is typically collected by digital channels. These digital channels are usually something we frequently use, including, email, text messages, GPS signals from mobile phones, and social media platforms.
Using big data, data scientists discover things that we do not know. Using a combination of computer science, predictive analytics, statistics, and machine learning, data science looks through large scare data sets to find original solutions to problems.
A data scientist is more concerned with finding questions to ask and less concerned with finding an answer.
Data analytics, on the other hand, help to provide operational insights into the business. Data analytics looks at the history of the data and from there will form a few conjectures that will help a business find a better solution to an issue that the company may be experiencing. Overall, data analytics helps to solve problems that we don't have the answers to and help provide solutions that will offer results.
The main difference between the two is that data science is more of an umbrella term. Unlike data analytics, data science can't give you an immediate answer. In data science, scientists focus on gathering massive quantities of data that requires a lot of effort to be filtered and analyzed to gain beneficial insights. Data analytics is more focused. Data analysts will sort through relevant data to with a goal in mind to find support.
Another difference between data science and data analytics is the tools that they use. In data science, researchers will require pretty advance and complex tools to handle and sort through of the data. In comparison, data analytics uses simple tools that focus on predictive and statistical modeling.
While these two disciplines have their differences, they can work seamlessly together. Where data science seeks out new questions, adding data analytics can help turn things we know into practical insights. Viewing data science and data analytics as a whole not only helps us to understand the information but how to analyze it.
This blog was originally posted on VincentGranville.io