Difference Between Big Data And Data Science

Difference Between Big Data And Data Science 1

Table of Contents: 

  • Introduction
  • What is Big Data?
  • Importance of Big Data
  • Examples of Big Data
  • Challenges faced by Big Data
  • What is data science?
  • Importance of Data Science 
  • Examples of Data Science
  • Challenges of Data Science
  • Differences between Big Data and Data Science
  • Concluding remarks


To begin with, Data science is an extension of statistics that deals with numerous large data sets with the assistance of computer science technology. Big data, on the other hand, is a vast collection of heterogenous data from a variety of sources, and you will not be able to get it in standard database formats. This means there would be no tabulation of data into a table, chart, or graph. 

What is Big Data?

Big Data is a combination of structured and unstructured data that an organization collects and uses to mine information. Big data is useful in machine learning projects, predictive modeling, and other advanced applications. A system used to store and process big data has grown to become an essential component of data management architectures in organizations. It is then combined with the tools that support big data analytics

Importance of Big Data

With the help of big data, companies can easily improve operations, provide enhanced customer service, and create personalized marketing campaigns to take other actions that ultimately lead to profit expansion. Businesses that can use big data have the edge over their competitors who do not use it that way. It gives an advantage of making swifter and more informed business decisions. 

For instance, with the help of big data, one gets valuable insights into customers that companies utilize to improve their marketing, advertising, and promotion strategies. This way, they will be able to increase the engagement of the customers better and also increase the conversion rates. Companies can enhance their responsiveness to customer demands and needs with the help of analysis of old and real-time data to better understand the changing preferences of consumers or buyers from a corporate backdrop.

Doctors and surgeons also take the help of big data to assist in the diagnosis of illnesses and medical problems in patients. Big data is also helpful to medical researchers in finding potential symptoms and risk factors. Additionally, healthcare institutions and governmental organizations also stay up-to-date with the help of big data by receiving information about infectious disease threats or outbreaks. This is made possible via a combination of data from electronic health records, the web, social networking sites, and other sources.

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Examples of Big Data

Some of the most common examples of the use of big data are enlisted below:

1. It is used in the energy industry by the oil and gas companies to locate sites suitable for drilling and monitor the pipelines’ operations. One can also use it to track the electrical grids. 

2. Companies providing financial services also benefit from big data as they use the system to manage risk and do real-time market data analysis. 

3. Organizations in the manufacturing and transportation sector use big data to manage their supply chains and optimize their delivery routes.

4. Government organizations and agencies use big data for purposes like emergency response, prevention of crime, and expediting smart city initiatives. 

Challenges faced by Big Data

When it comes to processing capacity issues, designing a big data role architecture is a major challenge that users face. The big data systems must be in such a way that it is tailored to the particular needs of an organization. This undertaking needs the organization’s IT and data management team to work together and form a customized set of tools and technologies. 

New and enhanced skills are also required to deploy and manage large data systems. These can be overcome with the help of cloud service, but IT managers need to keep an eye on cloud usage and ensure that it is not too expensive. One thing that needs to be noted is that this procedure is often complex and time-consuming. 

What is data science?

Data science can be defined as a process of cleaning, building, and structuring datasets for their analysis and extraction of meaning. One must not confuse data science with data analytics which is the analysis and interpretation of data. These procedures are quite similar and are also invaluable in the workplace. Data scientists are vested with the duty to write algorithms with the help of coding languages like R and SQL. These are then collected and used for the analysis of big data. If there is the correct design and thorough testing of these algorithms, one can do wonders by catching trends that humans would miss. The gathering and processing of data are also expedited with the help of data science. 

Importance of Data Science 

The Importance of Data science is a comprehensive concept that combines theories relating to data analysis, machine learning, and other related approaches to understand and analyze real-world occurrences. Data mining, predictive analysis, statistics, and other data analysis disciplines have progressed into data science. The vast area of data science makes complete and extensive use of techniques and ideas from other areas, including information science, statistics, and computer science. Machine learning, pattern recognition, probability models, visualization, data engineering, signal processing, and similar other techniques are a few used in data science.

Examples of Data Science

Some of the most common examples of Data Science are enlisted below:

1. It is used in the healthcare sector for medical image analysis. This procedure includes the detection of tumors, organ delineation, and then employing various methods and frameworks to find the optical parameters. 

2. With the help of data science, there has been a simplification and shortening of the drug development and discovery process.

3. Target advertising is one of the essential by-products of data science, which has positively affected the whole digital marketing spectrum.

4. One of the most wonderful applications of data science is the development of Augmented reality, which gives an exciting insight into the future. 

Challenges of Data Science

Most businesses gather enormous amounts of data without first taking into consideration whether or not there will going to be an actual utilization of any of it. The sole reason for this phenomenon is the availability of inexpensive storage and the worry organizations have over lost out on important insights that could be gained from the collected data. The downside of this data-collection craze is that companies end up collecting pointless data, which is of no use to them and detracts from the importance of actionable data. 

Finding the data assets that are relevant to the business strategy of the organizations becomes very challenging for data users as an outcome of it. Businesses must make sure they get pertinent information that they will use. To do that, it is essential to comprehend precisely what must be taken into consideration to inform decision-making, which alters depending on the type and size of the business.

Differences between Big Data and Data Science

Here are some of the most fundamental differences that one might find between Big Data and Data Science:

1. With the help of Big Data, organizations strive to improve their efficiency, understand the untapped market, and improve their competitiveness. On the other hand, data science is more focused on providing modeling techniques and styles to assess the potential of big data with precision.

2. A lot of companies collect data, and with the help of big data, the utilization of this data takes place. However, in turn, in data science, the extraction of valuable information is made possible with it. 

3. There are 3Vs of the big data guide dataset that includes velocity, volume, and variety. In the case of data science, one gets new and enhanced techniques to analyze the data efficiently. 

4. Data Science uses theoretical and practical approaches to extract information from big data, which also plays a crucial role in using the potential of big data. Big data is a pool of data with no credibility, which can only gain value with deductive and inductive reasoning. 

5. Data analysis of big data caters to a huge amount of data set, also called data mining. Whereas, when it comes to data science, one uses machine learning algorithms to design and develop statistical models to gain knowledge from a huge pile of data. 

6. Big data primarily deals with computer tools, software, and technology. On the other hand, data science is more focused on business decisions. 

Concluding remarks

There has been tremendous growth in data in this day and age. It is acting like a shining sunbeam and will stay there for the coming years. The presence of big data has helped businesses to thrive in this day and age. With the help of data science, one can better realize big data’s potential. 

Written by Srikanth

Passionate Tech Blogger on Emerging Technologies, which brings revolutionary changes to the People life.., Interested to explore latest Gadgets, Saas Programs

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