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Home General News

What’s the Difference Between Data Analytics and Data Science?

Max Logan by Max Logan
September 29, 2022
in General News
0

The use of data analytics is increasingly pushing the adoption of big data for business decision-making. Data engineers also keep leveraging data science methods to solve real-world problems. But in applying these two terms, many times, the lines blur. This poses a significant challenge if you plan to go into any of these fields. Check out this article for some differences between data analytics and data science.

Article Summary show
Data Analytics Defined
Data Science Defined
What’s the Difference?

Data Analytics Defined

Data Analytics

Raw data has very little meaning until it becomes analytics. Before any information, it needs to go through several cleaning and data preparation processes. The work of a data analyst begins after that. Data analytics is the process of leveraging data for business performance, market trends, competitive advantage, etc.

It involves a wide range of advanced analytics tools, techniques, and statistical models for the application of data. Today, combining these analytics tools, methods, and models for better business decisions constitutes business intelligence. Depending on the data analysis project at hand, data analytics approaches may vary. However, four broad data analytics types are established in the data management domain. They include:

  • Predictive Analytics: Predictive analytics is the basis of how machine learning technologies function. It combines historical data and assumptions to predict future events.
  • Descriptive Analytics: Some events occur with no records to show how they happened. Descriptive analytics employs data to draw connections between several data points to support meaningful descriptions of such events.
  • Prescriptive Analytics: Prescriptive analytics profer solutions to help organizations scale specific data problems or attain business targets and goals. When there’s a problem, prescriptive analytics help analysts answer the question of what can be done? It uses neural networks and recommendation engines to generate suggestions to help solve the problem.
  • Diagnostic Analytics: When an event occurs, descriptive analytics use data to understand why. According to Gartner, some attributes of diagnostic analytics include data discovery, data mining, and correlations.

Data Science Defined

Data science is the scientific study of data for efficient application. A data scientist can generate a hypothesis using scientific methods. And with computer science and statistical models, they can draw further analyses. There are many angles to the definition of data science. But the role data science plays in the entire data system can be a great place to start. Data science is the knowledge base experimenting and publishing new methodologies fuelling the data verse’s rapid innovation.

Data scientists first capture raw data with the several data mining tools available today. The type of data considered varies from one data warehouse to the other. After mining large amounts of data, the next step is to house the volumes in an efficient data warehouse.

Further processing occurs repetitively until the captured data is ready for data analysis. So it’s easy to say data science does all the dirty data work before a dataset can be deemed valid for a data analyst. The data science process is more cyclical than linear. A data scientist’s job never ends. After using data visualization to communicate data to analysts, they harness feedback inputting into the data cycle for another spin.

What’s the Difference?

Data Analytics

Many people use data science and data analytics interchangeably. But the definition scope can help differentiate between the two. Data science is an umbrella term comprising various disciplines to mine and prepare data. Data analytics, a sect of the more extensive process, helps extract actionable insights to solve problems.

Data science involves efforts to explore data but only leads to it. It provides insights and sometimes context for data professionals to ask the right questions. This direction serves as the basis for the actual exploration using data analytics software. Data analytics helps to answer the questions presented.

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Max Logan

Max Logan

Organizer. Social media ninja. Pop culture aficionado. Food nerd. Introvert. Spent 2002-2010 creating marketing channels for bassoons in Salisbury, MD. Prior to my current job I was marketing karma in Ocean City, NJ. Spent 2001-2007 getting my feet wet with barbie dolls in Salisbury, MD. Have some experience developing bacon in Phoenix, AZ. Set new standards for researching accordians for the underprivileged. Spent 2002-2007 merchandising soap scum in New York, NY.

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