The worldwide IT experts would agree that we enter an age of Big Data where lives can be analyzed, tracked, and optimized. Data Science and Big Data are two standard terms discussed a lot in forums, social media, or in almost all publications not because these technologies are in hottest trends, but because it has potential to improve operational efficiency, maximize profits, improve customer satisfaction, and help make better decisions in organizations. Both of them have some common elements, but in a big reality, Big Data is often a misinterpreted term in Data Science.

Today, we will explain the real difference between Big Data and Data Science to help you understand the core theories behind them.


What is Big Data?



The term that represents the large volume of data in both structured and unstructured form is known as Big Data. In other words, it refers to large data sets that need innovative technologies and techniques to use data efficiently.


What is Data Science?


Data Science is the research field that integrates programming skills, domain expertise, and knowledge of statistics and mathematics to extract meaningful insights from data. It is also popular with the name data-driven science because around 2.5 million terabytes of data are generated in a single day as the biggest asset of any company. This vast amount of data creates an opportunity for data scientists to analyze, extract, and maintain the company’s data using statistical methods and computer algorithms to support new ideas and solve many business problems.


Common Stereotypes About Big Data Analytics




Big Data is Valuable for Business Only:



Top FTSE 500 companies use data analytics to make future predictions, but you don’t need to purchase the biggest organization to use big data effectively. If you have a small corporation, you can still use Big Data to gain business profits. From small to large scale industries, everyone can use it today for different business niches to create new products, services, and experiences.


Big Data will Replace Data Warehouse:


Big Data is a repository that contains a great deal of data, but it is not clear what exactly we want to do with it. However, the purpose of a data warehouse is to make informed decisions, and we can use big data for data warehousing purposes. Big data and Data Warehouse are different from each other and, therefore, not interchangeable. So, it is entirely wrong that a big data solution will replace the use of Data Warehouse.


A Large Pool of Data is Required:




Due to the name of Big Data, many people assume that there will be a need for large pools of data to obtain actionable insights. However, large data sets are subject to redundant or duplicate entries. Quality matters a lot in data; if you fail it to acquire, then there is no benefit of gathering massive information.


Bring a Substantial Change:


The most common misinterpretation about big data is it will bring a substantial change in an organization. But it is itself not actionable. To obtain the real-insights from a lot of data, you will require a proficient data scientist to make meaningful decisions for any organization’s business. Nowadays, many professionals join a data science certification program and various online boot camps to learn a Python for Data Science, Advanced-Data Science course, Executive Program in Data Science training. The reason for attending data science classes is to understand big data and machine learning algorithms to design and develop statistical methods.


The Ultimate Guide to Key Differences – Big Data vs Data Science




  • Big Data is more related to computer tools, software, and technology. On the other hand, data science primarily focuses on business decisions.
  • Big Data is defined as the pool of data that has no value until it is evaluated through deductive and inductive reasoning. In contrast, Data science allows us to use theoretical and practical strategies to extract insights from big data.
  • The collected data of companies is enormous, and that belongs to big data. Though, data science is required from the utilization of the data to extracting valuable data.
  • There are three Vs of big data that are velocity, variety, and volume, on the flip side, tools and techniques used in data science to analyze the bulk of information.
  • The aim of using big data in organizations is to understand the untapped market, improve efficiency, and enhance competitiveness. Data science’s commitment is to provide modelling methods and techniques to examine the Big Data potential precisely.

To Sum Up

The media forms misleading perceptions of big data after analyzing the biggest data scandals and breaches of the 21st century. The world is the only big data problem because no one actually knows what it is all about. Due to that, Big Data is often misunderstood in Data Science.

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