The key differences between Big Data and Data Science

Organizations need big data to improve efficiency, understand new markets and increase competitiveness, so data science provides the methods to understand and use the potential of big data optimally.

Today, for organizations, there is no limit to the amount of data that can be collected; however, to use all this information to extract meaningful information for the organization’s decisions, data science is needed.

Large data is characterized by its speed, variety and volume (popularly known as 3V), while data science provides techniques for analyzing data characterized by 3V.

Differences between Big Data and Data science

Big data provides the potential for performance. However, extracting Big Data information to use its potential to improve performance is a significant challenge. Data science or Data science uses theoretical and experimental approaches in addition to deductive reasoning. It has the great task of discovering all the insightful hidden information of a complex network of unstructured data, which helps organizations realize the potential of big data.

The Big Data analysis performs the extraction of useful information from large volumes of data sets. Contrary to analysis, data science makes use of machine learning algorithms and statistical methods to train the computer to learn without much programming to make predictions from massive data. Therefore, data science should not be confused with big data analysis.

Big data is more related to technology (Hadoop, Java, Hive, etc.), distributed computing and analysis tools and software which is opposed to data science and focuses on the strategies for business decisions, data structures, and statistics methods.

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