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Data Science and Big Data

Big data and data science all involve data. For a layman, the two concepts may mean similar things. However, the two concepts mean two different things. This article will describe the two concepts and highlight the differences that define each idea.

Data Science

Data science describes a scientific method, which processes data using statistical and mathematical instruments via computer devices. Also, encompasses a wide range of field, which work in harmony to analyze and draw meaningful conclusions from raw data. The field of data science not only includes statistical and mathematical tools, but it involves, data collection tools, data mining process, data cleaning, and data processing.

Big Data

Big data is a form of data that is extremely large, complex, and continuous to grow exponentially as function of time. Due to the size and nature of big data, the usual data processing tools cannot be used to process or store such a data. Therefore, big data as a field is concerned with ways of making sense of such a large form of data.

The field of big data has become relevant and indispensable due to the enormous amount of information that the internet has created.

Difference Between Data Science and Big Data

To better under what data science and big data mean, it is important to understand the key difference between the two concepts. Some of the difference between data science and big data are presented under the headings below.


Since big data refers to raw data in extremely large quantities, businesses and institutions require as much big data as they can collect. The more the quantity of big data an institution can have at its disposal, the better the quality of analysis they are able to carry out. Likewise, the better decisions they can make about the direction of their business.

Data science, on the other hand, refers to the ways of making the best out of big data through analysis, generating relationships between variable, and drawing conclusions. Therefore, data science is the tool that makes normal data and big data useful.


Big data is commonly defined by three features, which are volume, velocity, and variety. These three features are critical when defining and categorizing big data. The volume indicates the amount of data, velocity indicates how fast the data can be processed, and variety indicates the types of data.

Data science provides the necessary tools to not only define big data based on these three features, but also to process, analyze, and extract meaningful information.


Data analysis that involves big data is based on information mining from large datasets, which expand exponentially. Such type of analysis require basic tools.

Nowadays, most data science analysis are conducted using machine learning, which combine mathematical and statistical tools to guide computer on deciphering information and making predictions from chunks of big data with minimal programming.


The major tools require in big data analysis include computer, programming languages, and software applications.

Data science tools are less involved with computing. Rather, strategies are used to make decisions.

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