Big Data Vs Data ScienceMay 28, 2019 Big Data
Currently, we are all witnessing an unprecedented growth of information generated around the world and on the Internet to give rise to the concept of big data. This concept refers to the large collection of heterogeneous data from different sources and is generally not available in standard database formats of which we usually know. Data Science and Big Data are the two terms commonly referenced throughout the literature, when discussing the potential benefits of enabling decision-making based on data. It is important to note that these latest trends are creating new employment opportunities and the demand for people with a certain set of data skills is increasing.
Big data covers all types of data, or that is, structured, semi-structured and unstructured information, which can easily be found on the Internet. In the case of unstructured data, unstructured data, in contrast, refers to data that does not fit perfectly into the traditional line structure and relational database column
Unstructured data: emails, videos, audio files, web pages, and social media messages.
Semi-structured data: Semi-structured data is a type of data that contains semantic labels, but does not fit structure associated with typical relational databases.
Structured data: The term structured data generally refers to data that have the length and format defined for big data. Examples of structured data include numbers, dates, and groups of words and numbers called strings.
Data refers to huge volumes of data of various types, that is, structured, semi-structured and unstructured. This data is generated through several digital channels, such as mobile, internet, social media, e-commerce sites, etc. The big date has proven to be very useful from the start, as companies began to perceive its importance for various commercial purposes. Data science deals with the breaking and cubing of large pieces of data, as well as finding insightful patterns and trends using technology, mathematics, and statistical techniques. Data scientists are responsible for uncovering the hidden facts in the complex unstructured data network, for use in making business decisions. Data scientists perform the above mentioned work by developing heuristic algorithms and models that can be used in the future for significant purposes.
Main differences between Big Data vs. Data Science:
Below are some of the main differences between the concepts of date and date of the date:
- Organizations need large volumes of data to improve efficiency, understand new markets and increase competitiveness, while data science provides the methods or mechanisms to understand and use Big Data potential in time.
- Currently, for organizations, not (in case there is an error in the amount of valuable data that can be collected, but to use all this data to extract meaningful information for organizational decisions, data science is necessary.
- Big data is characterized by its variety and volume of speed (popularly known as 3Vs), while data science provides the methods or techniques to analyze data characterized by 3Vs.
- Big data provides the potential for performance. Detailed large-date information to utilize its potential to improve performance is a significant challenge. Data science uses theoretical and experimental approaches in addition to deductive and inductive reasoning. It assumes the responsibility of uncovering all the hidden insight information from a complex mesh of data. unstructured data, giving support to organizations to perceive the potential Big Data.
Big Data Vs Data Science
|Basis||Big Data||Data Science|
|Meaning||Big data is the new science of understanding and predicting human behavior by studying large volumes of unstructured data. Big data is also known as ‘predictive analytics’.||Data science, or data-driven science, combines different fields of work in statistics and computation in order to interpret data for the purpose of decision making.|
|Concepts||A specialized area involving scientific programming tools, models and techniques to process big data.||Diverse data types generated from multiple data sources. Includes all types and formats of data|
|Basis of Formation||· Related data filtering, preparation and analysis.
· Capture context patterns from big data and develop models.
|· Online discussion forum.
· Data generated from system logs.
|Application areas||· Customer analytics
· Compliance analytics
|· Internet search.
· Digital Advertisements
|Approach||Big data has been used in the industry to provide customer insights for transparent and simpler products, by analyzing and predicting customer behavior through data derived from social media, GPS-enabled devices and CCTV footage.||Data Science is also used in Marketing, Finance, Human Resources, Health Care, Government Policies and every possible industry where data gets generated.|