计算机科学家和数据科学家之间的区别

计算机科学家：计算机科学家是拥有完整的计算机科学知识的人，即对计算和应用的研究。计算机科学家在该领域发明新技术，他们经常将这些技术应用于实际问题，例如科学或商业。这可能需要他们与其他专家(如工程师)合作。其中一些科学家可能专注于特定领域，包括编程或数据科学。

数据科学家：数据科学家将能够从头到尾进行数据科学项目。它们可以帮助存储大量数据、创建预测建模过程并呈现结果。他组织(大)数据。执行描述性统计和分析以开发洞察力、构建模型并解决业务需求。数据科学家的必备技能是数学和统计、领域知识和软技能、编程和数据库、通信和可视化。

下表列出了计算机科学家和数据科学家之间的差异：

Based on	Computer Scientist	Data Scientist
Definition	A computer scientist is a person who has knowledge of computer science that is the study of computation and application	A Data scientist will be able to take data science projects from end to end.They can help to store large amout of data, create predictive modelling processes and present the findings.
Skills	Software development Programming Information systems management	Mathematics Programming Communication
Importance	Computer scientist is very much necessary to understand the requirement and delivery the software product to end users without and vulnerabilities.	Nowadays, loads of data are coming from multiple areas/fields. Hence as data grows, expertise needed to analyze, manage and make it a useful solution for business
Methodology	For computer scientist, SDLC (Software Development Lifecycle) is the base which consists of requirements, software design, development, and software maintenance.	Methodologies for Data Scientist are similar to ETL process.
Tools	Design and Analysis Tools Database Tools Programming Languages Tools Web application Tools	Data visualization tools Data Analysis tools Database tools.
Requirements	Analyzing user requirement. Designer. Developer. Build and Release Engineer. Data Engineer.	Data scientist. Business Analyst. Data Analyst. Data Engineer and also Data specialist.
Approach	Approach for a Computer Scientist are: Waterfall Spiral V&V model Agile	Approach for Data Scientist are: Algorithms implementation Pattern recognition Data visualization Machine learning
Data Sources	User requirements, New features developments and also demand for the some functionalities etc.	Almost all website data can be considered for data source.Social Media, Business Apps, Transactions, Sensor Data, Machine Log Data etc.