Big Data vs Data Science

Definition of Big Data vs Data Science

Big data vs data science both are different terms, we are using this related to data. Unstructured, and structured data are all subjects of data science. It involves procedures like data preparation, analysis, and cleaning, among other things. Big data is used to describe large amounts of data that are ineffectively processed by the present, conventional applications. We are starting big data processing with raw data that is not aggregated.

Difference Between Big Data vs Data Science

Before Big Data, industries lacked the tools and resources to manage such a massive amount of data. The development of MapReduce and Hadoop, however, made it simpler for them to manage this type of data. On the other side, data science is the study of data via a scientific lens. It is more qualitative that makes use of several statistical techniques to uncover patterns in the data.

Data Science is the study of data analysis, whereas Big Data is the storage of data. It is critical to keep in mind that Data Science is a vast field of data operations, which also encompasses Big Data. A big data platform is necessary for the analysis of enormous amounts of data by a data scientist. Consequently, a data scientist should be knowledgeable about large data tools.

What is Big Data?

Big data refers to exceptionally huge data sets, as the term implies. These data sets have outperformed the powers of conventional data management solutions due to their size, complexity, and dynamic nature. In this sense, data lakes and warehouses have become the preferred methods for handling huge data, greatly outpacing the capabilities of conventional databases.

The following data sets fall under the category of big data are as follows.

• Data of the stock market.
• Data on social media.
• Data on games and sporting events.
• Emails, Research, and scientific data

What is Data Science?

Data science is complicated and covers many distinct fields and abilities. There is a vast and exponentially growing amount of data available everywhere. A number of the quantity of the data being processed, data science reflects the methods through which data is found, prepared, extracted, collated, processed, analyzed, and displayed. Big data is an application of data science that will be defined shortly.

Data science is an extremely complicated field, partly because it incorporates so many different academic fields and technological advancements. Mathematics, databases, signal processing, predictive analytics, and other fields are all incorporated within data science.

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

Below are the top 10 differences between Big Data and Data Science:

Key Differences Between Big Data vs Data Science

Let us look at the key differences between Big Data and Data Science:

• We are using big data to improve efficiency and competitiveness. While data science is used to provide methods and modeling techniques for evaluating big data potential in a prescribed way.
• Big Data is the storage of data. Data Science is a vast field of data operations, which also encompasses Big Data. The platform of big data is necessary for the analysis of enormous amounts of data by a data scientist.
• Only data management and storage are included in big data. To help with huge data analysis, additional parts like PIG and HIVE have recently been added framework of Hadoop. Additionally, more recent frameworks like Spark come built with analytical features for helping data science.
• Data analysis, insight extraction from the data, data visualization, and effective storytelling are all tasks that a data scientist must perform. On the other side, a Big Data Specialist creates, manages, and oversees the storing of enormous amounts of data. Data science is used in practical as well as theoretical approaches, while big data plays with a practical approach.

Big Data Requirement

Big data must thoroughly research the topic to make the information pertinent and useful for making wise business decisions. The following are the criteria for big data.

• Data processing, which involves the categorization and collection of raw data, is the initial prerequisite for big data.
• Predictive applications are the second prerequisite for big data, as we must recognize managerial tasks.
• To provide flexibility, we must use analytics tools, which comes with a variety of packages.
• Research on risk analysis for a specific activity is necessary.
• The big data analytical tools include a module called decision management that executes the business process.

Data Science Requirement

A business challenge can be turned into a study project with the aid of data science, and then back into a workable solution. The development of big data and quantitative statistics has given rise to the term data science. To use data science we required below skills are as follows.

• Computing and statistical analysis
• Deep learning
• Machine learning
• Data wrangling
• Data visualization
• Statistics
• Big data
• Programming
• Large data set processing
• Mathematics

To work on data science we need to install the required software and tools in our system. Also, we need to be familiar with the language of data science.

Comparison Table of Big Data vs Data Science

The table below summarizes the comparisons between Big Data vs Data Science:

Big Data	Data Science
Big data is a technique used to maintain, collect and process large information.	Data science is nothing but an area.
Big data involves extracting information from big data sets.	Data science involves processing and utilizing data in various operations.
Big data is a less conceptual term as compared to data science.	Data science is a more conceptual term as compared to big data.
Big data is a technique used to track and discover trends in large data sets.	Data science is the field of study of mathematics and computer science.
The goal of big data is to make data usable and vital.	Data science aims to build the dominant products of the venture.
Mainly used tools in big data are Hadoop and spark.	We are mainly using tools in data science are R, Python, and SAS.
Big data consist the mining activities.	Data science consists the data cleaning, visualization, and other multiple techniques.
Big data is mainly used for business purposes.	Data science is used mainly for scientific purposes.
Big data is mainly involved in the data processing.	It mainly focuses on the science of data.
Big data is working on structured as well as non-structured data.	Mainly it is working on structured data.

Purpose of Big Data

Big data is made up of different types of data rather than being restricted to a single type. Regardless of data structure, big data includes a variety of data types, including image and audio data as well as tabular databases. New data is frequently created, this is very common when working with constantly growing data sources like social media, and the Internet of Things.

Due to the size and complexity of big data, there will unavoidably be some inconsistencies in the data. To effectively handle and process huge data, you must take unpredictability into account. Big data analysis output value is assessed depending on particular business goals, which can be arbitrary.

Purpose of Data Science

Since data science encompasses all aspects of data, it is possible to employ any tool or technology in some way during the data science process. The study of diverse scientific techniques that draw valuable conclusions from a massive amount of data is known as data science. Data scientists can also use it to glean hidden patterns from unprocessed data.

We are using data science by using multiple tools on structured data. Data science is basically used on structured data. In data science, we are using python, R, and SAS languages. We are using data science in multiple areas mainly for retrieving scientific data. To use data science we need to have knowledge of multiple fields of data science. We need to build the SQL query while using data science tools.

Conclusion

We are using big data to improve efficiency and competitiveness. While data science is used to provide methods and modeling techniques for evaluating big data potential in a prescribed way. Big data is used to describe large amounts of data that are ineffectively processed by the present, conventional applications. We are starting big data processing with raw data that is not aggregated.

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