Data Scientists

• convert business problems into an analytics solution
• using their consulting skills, business knowledge to analyze client business issues,
• formulate hypotheses and test conclusions to determine appropriate solutions methods.
• A solid foundation typically in statistics, modeling, operations research, computer science and applications, and math. What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization. A data scientist is effective at deploying an analytics solution, thereby realizing business value. Whereas a traditional data analyst may look only at data from a single source – a CRM system, for example – a data scientist will most likely explore and examine data from multiple disparate sources. The data scientist will extract, transform, and combine all incoming data with the goal of discovering a previously hidden insight, which in turn can provide a competitive advantage or address a pressing business problem. A data scientist does not simply collect and report on data, but also builds statistical models, determines what it means, then recommends ways to apply the data. Data scientists are inquisitive: exploring, asking questions, doing "what if" analysis, questioning existing assumptions and processes. Armed with data, modeling expertise, and analytical results, a top-tier data scientist will then communicate informed conclusions and recommendations across an organization's leadership structure.

数据科学家业务问题转化为一个分析解决方案使用他们的咨询技能、业务知识来分析客户业务问题,制定假设和测试的结论来确定适当的解决方法。通常一个坚实的基础在统计、建模、运筹学、计算机科学和应用程序,和数学。数据科学家之间的区别是强大的商业头脑,加上沟通的能力发现业务和IT领导人的方式可以影响一个组织的方法一个业务的挑战。好的数据科学家将不仅仅是解决业务问题,他们会选择正确的组织最有价值的问题。数据科学家在部署一个有效的分析解决方案,从而实现商业价值。而传统的数据分析师可能只看单一来源——一个CRM系统的数据,例如,数据科学家将最有可能的探索并检查来自多个不同数据源的数据。数据科学家将提取、转换和将所有输入数据的目的,发现以前隐藏的见解,进而可以提供一个竞争优势或地址一个紧迫的业务问题。数据科学家并不简单地收集和报告数据,建立统计模型,确定是什么意思,然后推荐应用数据的方法。数据科学家们好奇:探索,问问题,“如果”分析,质疑现有的假设和流程。手持数据,建模技术,分析结果,顶级数据科学家将沟通告知结论和建议在一个组织的领导结构。

总结：

Data Scientist

运用咨询技能，业务知识能将业务问题转化为分析解决方案