Did you know that we generate roughly 2.5 quintillion bytes of data in a day?
Did you know that Google processes roughly 3.5 billion searches in a day?
Did you know that almost 1.5 billion users are active on Facebook every day?
Did you know that there are 95 million photos and videos shared on Instagram every day?
Did you know users tweet 456 million tweets on Twitter every day?
And this is just the tip of the iceberg. There are more than 3.7 billion human beings using the internet every single minute at any given point in time all over the world. Hence each individual is generating enormous amounts of data every day, and with a constant increase in electronic devices, this number is on a continuous rise.
Thus, data has become an integral part of our lives. Big Data and Data Science have become common terms now. And yet being a relatively new development, the differences between these two fields are often misunderstood. To understand the differences between Big Data and Data Science, first, we need to define them.
Data science is a multidisciplinary field of data inference, algorithm development, and technology which is used to solve analytically complex problems related to large volumes of data. On the other hand, Big data refers to any voluminous amount of structured, semi-structured or unstructured data that has the potential to be mined further for information. They are both interlinked by one simple term, i.e. data, yet they are different in the way they are approached.
Given below is a table that elaborates these differences:
| Points of Differentiation | Big Data | Data Science |
| Purpose | Used for enormous volumes of data which cannot be processed by traditional database programming. | Used for harnessing the potential of big data for business decisions involving scientific activity like data mining. |
| Concept | It includes multiple types and formats of data from various sources. | It is a specialized area of study involving scientific programming tools, models and techniques to process large data sets. |
| Composition | Big data can be collected from internet users and electronic devices like sensors, RFID, etc. It also includes audio/video streams including live feeds and online discussion forums. Moreover, data generated in organizations like transactions, spreadsheets, emails, system logs etc. | Data science applies scientific methods to extract knowledge from big data. There are different ways to do this like filtering, preparation, and analysis. Once this data is analyzed, new models and working apps are developed. |
| Areas of Application | It can be applied to various industries like financial services, telecommunications, optimizing business processes, performance optimization, health and sports, retail and commerce, research and development, security and law enforcement etc. | Data science can be applied to every industry depending on the requirements of the data. For example in internet search, digital advertisements, search recommenders, image and speech recognition, fraud, risk detection, web development etc. |
| Approach | Using big data develops business agility, helps to gain competitiveness, leverage datasets for business advantage, establish realistic metrics and ROI. It also helps to achieve sustainability in an understated market and acquire new customers. | Data science Involves the use of mathematics, statistics, and other tools to develop techniques/ algorithms for data mining, programming skills and Hadoop platforms. It also utilizes data acquisition, preparation, processing, publishing, data visualization and prediction etc. |
Considering the current scenario of the world when data is a part of each and every minute of our lives, there is a surge of jobs for professionals specializing in big data and data science. To make a career in these fields, you need the right certifications and knowledge which you can easily find at Global Tech Council. Forbes Magazine has estimated that new data will be generated at the rate of 1.7 million MB per second by 2020. Thus, it’s no wonder that big data and data science are here to stay!
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