数据科学：数据科学可能是一个包含处理大量信息、创建算法、使用机器学习等以得出商业见解的空间。它结合了大量数据的处理。包含不同的句柄以从源中推断信息，例如提取数据、清理数据，然后将其转换为客户端诱人的安排，可以鼓励利用数据执行任务。
数据科学包括利用机器人化策略来分析大量信息并从中提取信息。
软件工程：软件工程的特点是准备分析客户需求，然后规划、构建和测试能够满足这些需求的程序应用程序。术语软件工程是两个词的项目，程序和工程。该程序可以是坐标程序的集合。软件依赖于设计者使用任何不同的特定计算机语言编写的精心组织的启发和代码。计算机程序和相关文档，例如先决条件、计划模型和客户手册。工程是应用逻辑和可行的信息来编造、计划、构建、跟上和推进系统、形式等
下表列出了数据科学和软件工程之间的差异：
| Data Science | Software Engineering |
|---|---|
| In Data Science, ETL is the method for information extraction, changing it into a coherent arrange that’s simple to get it and stacking it into a framework for preparing. | SDLC (Software Development Life Cycle) shapes the premise of software engineering. |
| Data Science takes after the process-oriented approach and permits design acknowledgment, calculations usage etc. | Software Engineering is framework-oriented that includes Waterfall, Spiral, agile systems and more. |
| Data science includes data visualization tools, data analytics tools, and database tools. | Software engineering includes programming instruments, database devices, plan instruments, CMS devices, testing devices, integration apparatuses, etc. |
| Data science includes stages like Hadoop, MapReduce, Start, Information stockroom or Flink etc. | Software Engineering includes stages like information modeling, commerce arranging, programming, upkeep, venture administration, turn around designing, etc. |
| fundamental information of domains, algorithms, big data handling, data mining, structure or unstructured information, insights, likelihood, AI, machine learning, etc. | knowledge of core programming languages, testing or construct tools, setup tools, discharge administration tools, etc. |
| Roles in Data science Data scientist, Data Analyst, Business Analyst, Data Engineer, and Big Data specialist | Roles in Software engineering Release Engineer, Testers, Data Engineer, Product managers, Administrators, and cloud consultants. |
| Data science is Process Oriented | Software engineering is methodology Oriented |
| Data Sources in Data science are Sensor Data, Transactions, Public Data Baking etc | Data Sources in Software engineering are nd-user needs, New features development etc |