大数据：是指大型组织和企业获得的庞大、庞大或海量的数据、信息或相关统计数据。由于难以手动计算大数据，因此创建和准备了许多软件和数据存储。
它用于发现模式和趋势，并做出与人类行为和交互技术相关的决策。
数据科学：数据科学是一个领域或领域，它包括并涉及处理大量数据，并将其用于构建预测性、规范性和规范性分析模型。它是关于挖掘、捕获、（构建模型）分析（验证模型）和利用数据（部署最佳模型）。
它是数据和计算的交集。它融合了计算机科学、商业和统计学领域。
下表列出了大数据和数据科学之间的差异：
| Data Science | Big Data |
|---|---|
| Data Science is an area. | Big Data is a technique to collect, maintain and process the huge information. |
| It is about collection, processing, analyzing and utilizing of data into various operations. It is more conceptual. | It is about extracting the vital and valuable information from huge amount of the data. |
| It is a field of study just like the Computer Science, Applied Statistics or Applied Mathematics. | It is a technique of tracking and discovering of trends of complex data sets. |
| The goal is to build data-dominant products for a venture. | The goal is to make data more vital and usable i.e. by extracting only important information from the huge data within existing traditional aspects. |
| Tools mostly used in Big Data includes Hadoop, Spark, Flink, etc. | Tools mainly used in Data Science includes SAS, R, Python, etc |
| It is a super set of Big Data as data science consists of Data scrapping, cleaning, visualization, statistics and many more techniques. | It is a sub set of Data Science as mining activities which is in a pipeline of the Data science. |
| It is mainly used for scientific purposes. | It is mainly used for business purposes and customer satisfaction. |
| It broadly focuses on the science of the data. | It is more involved with the processes of handling voluminous data. |