数据科学：对来自组织存储库中数据的信息流的详细研究称为数据科学。数据科学是通过应用分析、编程和业务技能从原始和非结构化数据中获得有意义的见解。
数据科学生命周期包括：
- 数据发现：搜索不同的数据源并捕获结构化和非结构化数据。
- 数据准备：将数据转换为通用格式。
- 数学模型：使用变量和方程建立关系。
- 采取行动：收集信息并根据业务需求得出结果。
- 交流：将调查结果传达给决策者。
数据工程：数据工程专注于大数据的应用和收获。数据工程侧重于数据收集和分析的实际应用。在此数据中，数据被转换为一种有用的分析格式。数据工程在很多方面与软件工程非常相似。从具体目标开始，数据工程师的任务是将功能系统组合在一起以实现该目标。
下表列出了数据科学和数据工程之间的差异：
| S.No. | Data Engineering | Data Science |
|---|---|---|
| 1. | Develop, construct, test, and maintain architectures (such as databases and large-scale processing systems) | Cleans and Organizes (big)data. Performs descriptive statistics and analysis to develop insights, build models and solve business need. |
| 2. | SAP, Oracle, Cassandra, MySQL, Redis, Riak, PostgreSQL, MongoDB, neo4j, Hive, and Sqoop. Scala, Java, and C#. | SPSS, R, Python, SAS, Stata and Julia to build models. Scala, Java, and C#. |
| 3. | Ensure architecture will support the requirements of the business | Leverage large volumes of data from internal and external sources to answer that business |
| 4. | Discover opportunities for data acquisition | Employ sophisticated analytics programs, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling |
| 5. | Develop data set processes for data modeling, mining and production | Explore and examine data to find hidden patterns |
| 6. | Employ a variety of languages and tools (e.g. scripting languages) to marry systems together | Automate work through the use of predictive and prescriptive analytics |
| 7. | Recommend ways to improve data reliability, efficiency and quality | Communicating findings to decision makers |