Big Data Vs Data Science

Big Data vs Data Science

What is Big Data

Big data is a data that contains more variety reaching increasing volumes and with increasing speed. This is known as the three vs Simplifying, big data is a larger and more complex data set, especially from new data sources. These data sets are so bulky that traditional data processing software can not manage them. But these massive volumes of data can be used to solve business problems that you could not resolve before.

Types Of Big Data

Structured

Any data that can be stored, accessed and processed in a fixed format is called “structured” data. Over time, the talent in computer science achieved greater success in the development of techniques to work with that type of data (where the format is well known in advance) and also extract value from it. However, today, we are anticipating problems when the size of this data grows on a large scale, typical sizes are in fashion for several zettabytes.

Unstructured

Unstructured data refers to data that does not have any specific form or structure. This makes it very difficult and slow to process and analyze unstructured data. E-mail is an example of unstructured data.

Semi-structured

Semi-structured data refers to data that contains the two formats mentioned above, that is, structured and unstructured data. To be precise, it refers to data that, although they have not been classified in a specific repository (database), contain vital information or marks that segregate individual elements within the data.

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Big Data Characteristics

  1. Volume:  Volume is the amount of data generated that must be understood to make decisions based on data. A text file has some kilobytes, a sound file has some megabytes, while a full movie has a few gigabytes.

Example: Amazon processes 15 million clicks on customers per day to recommend products.

2. Velocity: Velocity measures the speed with which the data is produced and modified and the speed with which it needs to be processed. A greater number of data sources, both the speed of the unit generated by the machine and by the human.

Example: 72 hours of video are sent to YouTube every minute, that’s the speed.

3. Variety: Variety defines data from new sources – inside and outside a company. It can be structured, semi-structured or unstructured.

What is Data Science?

Data science is a detailed study of the flow of information from colossal amounts of data present in the repository of an organization. It involves obtaining significant insights from raw and unstructured data that are processed through analytical, programming and business skills.

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Benefits Of Data Science

Most important business value:

The main advantage of listing the data science in an organization is a of decision making faster and better. These data-driven decisions, in turn, lead to greater profitability and better operational efficiency, business performance and workflows. Data Science helps to identify and refine the target audience in organizations aimed at the client.

Identification and refinement of target audiences:

Data collected from Google Analytics for customer surveys must be analyzed to identify demographic information. Organizations can also adapt services and products to customer groups and help profit margins flourish.

Better risk analysis:

Predictive analysis, driven by Big Data and Data Science, allows users to digitize and analyze news reports and social media feeds to stay updated on the latest trends in the sector. In addition, it also promotes detailed health tests on its suppliers and clients. This is useful for assessing risks and taking the necessary steps for mitigation in advance.

Data Science Basics

Data science allows the use of theoretical, mathematical, computational methods and other practical methods to study and evaluate data. The main objective is to extract necessary or valuable information that can be used for various purposes, such as decision making, product development, trend analysis and forecasting. A data scientist is an individual who practices data science.

In addition, the concepts and processes of data science are derived from data engineering, statistics, programming, social engineering, data warehousing, machine learning and natural language processing, among others.

Data science is important because knowledge is important. … Enables professionals with data management technologies such as Hadoop, R, Flume, Sqoop, Machine Learning, Mahout Etc. The knowledge and skill of the skills are an additional advantage for a better and competitive career.

Basis for Comparison                       Big Data      Data Science
Meaning  Large volumes of data that can not be manipulated by traditional database programming

Characterized by volume, variety and speed

A scientific activity focused on data

Approaches to processing big data

Takes advantage of the Big Data potential for business decisions

Similar to data mining

·Concept  Various types of data generated from multiple data sources

Includes all types and data formats

 A specialized area that involves tools, models and techniques of scientific programming to process large data

Provides techniques to extract ideas and information from large data sets

Supports organizations in decision making

  Basis of formationInternet / traffic users

Electronic devices (sensors, RFID, etc.)

Audio / video transmissions, including live feeds

Online discussion forums

Data generated in organizations (transactions, databases, spreadsheets, and -mails, etc.)

Data generated from system records

 Apply scientific methods to extract knowledge of big data

Related to filtering, preparation and analysis of data

Capture complex big data patterns and develop models

Work applications are created by programming developed models

 Application areas  Financial services

Telecommunications

Optimization of business processes

Optimization of performance

Health and sports

Improve tradeResearch and development

Security and law enforcement

 Search Internet

recommenders digital signage

Research

Image Recognition / Speech

Fraud detection

Web development risks

Other miscellaneous / utilities areas

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