Big Data Vs Data Science | Comparison Of Big Data Vs Data Science

By using traditional data analysis methods it takes time and effort to achieve the big data approach. To extract insights and information the unstructured data needs specialized data that models techniques, tools and systems which is required by the organizations. The right approach which applies math’s and statistical ideas and computer tools for big data are Data Science. It’s a dedicated field that gathers different areas like statistics, mathematics, intelligent data capture techniques, mining and programming to make and align big data for clever analysis to bring out the insights and information. You will learn more about it at KAHE, Top IT College in Coimbatore.

These days we are observing an unparalleled development of information that is generated all over the world and on the internet to emerge in the concept of big data. Data science is a very challenging area because of its complexities that include gathering and applying various methods, algorithms and complex programming techniques to execute smart analysis in big volumes of data. That’s why data science has made progress from big data, or big data and data science are not separable.

The large collection of heterogeneous data from various sources refers to this concept and it’s not accessible in the usual database formats that we all are familiar with. Usually all huge data compasses types of data namely structured, semi-structured information which is easily accessible on the internet. The basics of it are as important as the high-quality points which will be covered during your study tenure at KAHE, one of the Top Colleges in Coimbatore.

Big data includes:

  • Unstructured data – emails, social networks,  blogs, tweets, digital images, digital audio/video feeds, mobile data,  online data sources, sensor data, web pages, etc.
  • Semi-structured – XML files, system log files, text files, etc.
  • Structured data – RDBMS (databases), OLTP, transaction data, and other structured data formats.

That’s why all data and information disregarding its type or format can be considered as big data. Big data processing begins with collecting data from multiple sources. Experts have suggested learning the roots of it from KAHE, Top Engineering College in Coimbatore.

Head to Head Comparison Between Big Data and Data Science (Infographics)

Below are the top 5 comparisons between Big Data vs Data Science

Key Differences Between Big Data and Data Science

1. Meaning

Big data: Big data is the big amount of data that cannot be handled by using traditional database programming. It’s characterised by volume, variety and velocity.

Data Science: Data science is focused on scientific activity. Its approach is towards the big data process. It harnesses the potential of big data for business decisions. And it’s similar to data mining. You will learn more about this in your courses at KAHE, Top Computer Science Engineering College in India.

2. Concept

Big data: Big data is a diverse data type that is generated from multiple data sources. It includes all types and formats of data.

Data science: It is a specialised area that involves scientific programming tools, models and techniques to process big data. It provides the techniques to extract insights and information from large data sets. It also supports the organisations in decision making.

3. Basis of Information

Big Data: it gets its information from the internet users or traffic. It includes electrical devices, audio streams including live feeds, online discussion forums. The data can be generated from system logs and in organizations, however, the basics to extract the same can be learned at KAHE, Best CSE College in Coimbatore.

Data science: It applies scientific methods to extract knowledge from big data. It’s related to data filtering and analysis. Data science captures complex patterns from big data and develops models.

4. Application areas

Big data: The application includes financial services, telecommunications, optimization of business processes, process optimization, health and sports, research and development, security and law enforcement.

Data Science: it includes internet search, digital advertising, search recommendation, image or speech recognition, fraud and risk detection, web development and other miscellaneous areas or utilities.

5. Approach

Big data: It approaches to develop business agility, gain competitiveness, leverage datasets for business advantage. It also establishes realistic metrics and ROI, which you can seek interest in at KAHE, Top colleges in Coimbatore for Arts and Science.

Data science: it involves extensive use of mathematics, statistics, and other tools. It also deals with state of art techniques, algorithms for data mining. Data acquisition, preparation, processing, publishing, data visualization.

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