Big Data Vs Data Science
Big Data Vs Data Science
There may be not much a difference, but big data vs data science has always instigated the minds of many and put them into a dilemma. Today, we will reveal the real difference between these two terms in an elaborative manner which will help you understand the core concepts behind them and how they differ from each other. First of all, data science is an evolutionary extension of statistics that deals with large datasets with the help of computer science technologies. Many confuse Data science with absolutely wrong machine learning. Although machine learning is a subset of Data science, they are not the same.
On the other hand, big data deals with the vast collection of heterogeneous data from different sources and is not available in standard database formats that we are aware of. This implies that the data won’t be tabulated into a table or chart or graph.
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Big data classifies data into unstructured, semi-structured, and structured data.
- Unstructured data – social networks, emails, blogs, digital images, and contents
- Semi-structured data – XML files, text files, etc.
- Structured data – RDBMS, OLTP, and other structured formats.
While structured data is quite simple to understand, unstructured data required customised modelling techniques to extract information from the data which is done by the help of computer tools, statistics, and other data science approaches.
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Key differences – Big Data vs Data Science
There are some major differences which we should talk about when our topic is Big Data vs Data Science .
- Big data is used by organisations to improve the efficiency, understand the untapped market, and enhance competitiveness while data science is concentrated towards providing modelling techniques and methods to evaluate the potential of big data in a précised way.
- The amounts of data that can be collected by the companies are huge, and they pertain to big data but utilisation of the data to extract valuable information, data science is needed.
- The 3Vs of the big data guide dataset and is characterized by velocity, variety, and volume but the data science provides techniques to analyze the data.
- Data science supposedly uses theoretical as well as practical approaches to dig information from the big data which plays an important role in utilizing the potential of the big data. Whatsoever, big data can be considered as the pool of data which has no credibility unless analysed with deductive and inductive reasoning.
- Big data analysis caters to a large amount of data set which is also known as data mining, but data science makes use of the machine learning algorithms to design and develop statistical models to generate knowledge from the pile of big data.
- Data science focuses more on business decision whereas Big data relates more with technology, computer tools, and software.
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Comparison of Big data Vs Data Science
Basis |
Data Science |
Big Data |
| Meaning | Skewed towards the scientific approach of interpreting the data and retrieves the information from a given data set | Revolves around the huge volumes of data which cannot be handled using the conventional data analysis method |
| Concept | Obtained with big data is heterogeneous that indicates a diversified data set which has to be per-cleaned and sorted before running analytics on them | Scientific techniques to process data, extract information and interpret results which help in the decision-making process |
| Formation | Internet users/ traffic, live feeds, and data generated from system logs | Data filtering, preparation, and analysis |
| Application areas | Internet search, digital advertisements, text-to-speech recognition, risk detection, and other activities | Telecommunication, financial service, health and sports, research and development, and security and law enforcement |
| Approach | Uses mathematics and statistics extensively along with programming skills to develop a model to test the hypothesis and make decisions in the business | Used by businesses to track their presence in the market which helps them develop agility and gain a competitive advantage over others |
Conclusion
The current growth trend in the data segment of the industry is increasing and it acts as a shining sunbeam on big data which indicates that big data is here to stay in the coming years. The optimum utilization of the data will help many businesses thrive. According to the estimates of Forbes magazine, the data generation speed will be at the rate of 1.7 million MB per second which shows an immense potential in the analytics field. If managed effectively by the organizations, big data can help them to evolve rapidly at a pace faster than the competitors. Data science is also set to be present in the forthcoming years and will be known for its role in realizing the potential of the big data.
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