Big data: This is large, complex, or voluminous data or information, or the relevant statistics, that has been acquired by large companies and ventures. Because it is hard to calculate big data manually, many software and data storages are created. It’s used to identify patterns and trends, and to make decisions about human behavior and interaction technology.

Data Science: Data Science is a field that deals with large amounts of data. It uses this data to build predictive, prescriptive, and prescriptive models. It involves digging, capturing (building the model), analyzing (validating the model), and using the data(deploying the best model).
It is a combination of data and computing. It is a combination of Statistics, Computer Science, and Business.

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Traditional data analysis methods are not able to handle big data. Unstructured data, on the other hand, requires special data modeling techniques, tools, and systems that can extract insight and information according to organizations’ needs. Data science is a scientific approach to big data processing that uses mathematical and statistical ideas. Data science is a field that integrates multiple disciplines such as mathematics, statistics, intelligence data capture techniques, data cleansing and mining, and programming. It prepares and aligns big data for intelligent analysis to extract insights.

We are all witnessing an unprecedented increase in information worldwide, and online. This is leading to the idea of big data. Data science is a complex area because of the complexity involved in applying and combining different algorithms and programming techniques to intelligently analyze large amounts of data. Data science evolved from big data. Big data and data science are interrelated.

This refers to a large amount of heterogeneous data that is not available in the standard database formats we know. Big Data includes all data types, including structured, semi-structured, and unstructured data that can easily be found online.

Big data includes:

• Unstructured Data– social media, emails, blogs and tweets, as well as digital images, digital audio/video streams, online data sources, mobile information, sensor data, web pages and so forth.
• Semi-structured – XML files, system log files, text files, etc.
• Structured Data – RDBMS, OLTP, transactional data and other structured formats.

Big data can therefore be understood to include all information and data, regardless of their format or type. The bulk of big data processing involves aggregating data from many sources.

There are key differences between big data and data science

Below are some key differences between data science and big data concepts.

• Big data is essential for organizations to increase efficiency, understand new markets and improve competitiveness. Data science, however, provides the tools and methods to quickly understand and use the huge potential of big data.
• Organizations have unlimited access to valuable data. However, to make meaningful decisions based on this data, data science is required.
• Big data is defined by its velocity variation and volume (commonly known as the 3Vs), while data sciences provides methods and techniques for analyzing data that are characterized by the 3Vs.
• Performance is possible with big data. It is difficult to extract the insights from big data in order to maximize its potential to improve performance. Data science employs both experimental and theoretical approaches, as well as deductive and inductive reasoning. It is responsible for uncovering all the hidden insights from unstructured data, thereby enabling organizations to harness the power of big data.
• Big data analysis is the process of mining useful information from large amounts of data. Data science, contrary to analysis, uses machine learning algorithms and statistical techniques to train the computer without programming to predict big data. Data science should not be confused for big data analytics.
• Big Data relates more technology (Hadoop Java Hive, Hive, etc.). Analytics tools and software, distributed computing, and distributed computing. This contrasts with data science, which focuses more on business strategies, data dissemination using statistics, mathematics and data structures, and methods, as mentioned previously.

You can see that big data include data science from the differences between data science and big data. Data science is an integral part of many areas. Data science uses big data to gain useful insights and make smart decisions. Data science is therefore included in big data, not the reverse.

Conclusion

This post explores the emerging field of data science and big data. According to Forbes Magazine estimates, big data will continue to grow in the future because of current trends in data growth. By 2020, data will be generated at a rate of 1.7million MB per second. Organizations must manage this huge potential of big data. This article will explore the role of data science in realizing big data’s potential. Data science is constantly evolving with new techniques that can be used to support data scientists into the future.