Data Science vs. Data Analytics: What’s the Difference?

Data Science vs. Data Analytics: What’s the Difference?

The functions of data science and data analytics are interconnected. Both fields focus on understanding the data that companies have and finding ways to better analyze it. These processes provide actionable insights for leadership to make more effective business decisions.

However, data science and data analytics significantly differ in their approaches to data and the results they uncover. This is why data scientists and data analysts add different forms of value to an organization.

The Goal of Data Science

Data science uses large sets of raw, unstructured data to uncover actionable insights. Data scientists use computer science, predictive analytics, statistics, and machine learning to parse through datasets. The goal is to determine important questions about the business without finding the answers.

Data scientists ask questions and find potential avenues to study. They examine disparate and disconnected data sources to create initial observations, predict trends, and find more effective methods to study information. Improvements in how the information is sorted and understood are especially beneficial for fields such as modeling, machine learning, and artificial intelligence.

The Goal of Data Analytics

Data analytics processes and performs statistical analysis on existing datasets. Data analysts create methods to capture, process, and organize data to find actionable insights for current problems. The analysts then establish the best way to present the data.

Data analysts work to answer questions and solve problems that already are known. The results typically lead to immediate improvements.

Differences Between Data Science and Data Analytics

The main difference between data science and data analytics is the scope of the fields:

  • Data science describes a group of fields that involve mining large datasets.
  • Data analytics is a field focused on uncovering actionable insights that can be immediately applied to answer existing questions.

Exploration in data science and data analytics is significantly different:

  • Data science uses unstructured methods to parse through massive datasets and uncover insights. These insights are used to determine which questions to ask.
  • Data analysis uses existing data to answer specific questions.

The major fields that use data science and data analytics are different:

  • Data science is widely used in machine learning, artificial intelligence, search engine engineering, and corporate analytics.
  • Data analytics is widely used in healthcare, gaming, travel, and other industries with immediate data needs.

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