# 经济代写|供应链管理代写supply chain management代考|ASCl2022

## 经济代写|供应链管理代写supply chain management代考|THE STATE OF BIG DATA EDUCATION

The use of the term, “unicorn,” to describe a data scientist, has been done so because of how difficult it currently is to find an individual with the analytical, technical, and business skills the role requires (Bakhshi et al., 2014, p. 32). Dwoskin (2014) stated that the two websites, LinkedIn ${ }^{\mathrm{TM}}$ and SimplyHired.com, listed approximately 30,000 openings for positions with “Data Scientist” in their titles. Though a data scientist does not always require a postgraduate degree, it is quite common that they do. In 2012, only 2500 doctoral degrees were awarded in statistics or computer science (Dwoskin, 2014). Jeonghyun Kim (2016) showed that only twenty-five schools in the United States provide postgraduate classes that were data analytic-specific. Table $10.1$ lists the number of schools offering data-centric programs:

With over 4726 universities in the United States offering multiple program degrees in a multitude of disciplines, the number of advanced degrees offered in data-centric programs, as listed in Table 10.1, is tiny, but growing.

## 经济代写|供应链管理代写supply chain management代考|DATA SCIENTIST VS DATA ANALYST

The title, “Data Scientist,” is a relatively new title that differs from the traditional data analyst role provided to those who find trends and model results of traditional business intelligence systems. Where data analysts will use structured query language $(\mathrm{SQL})$ to pull information from relational databases, data scientists use $\mathrm{SQL}$ as well as the machine language tools to use statistical models to find correlations between different variables (Harris, Shetterley, Alter, \& Schnell, 2014). SAS (2015) differentiates data scientists from even statisticians by explaining that data scientists move beyond descriptive statistics and the reporting of past results to predictive modeling of what is likely to happen in the future. Statistician and data analyst roles are traditionally processing type positions. Business leaders ask for supporting statistics or diagrams and ask for the information from these roles. To be a data scientist, one must be front and center in the business goal discussion (Redman, 2013).

Chen, Chiang, and Storey (2012) expand on the unique responsibilities of the data scientist by listing knowledge of accounting rules, finance, management practices, marketing approaches, logistic methods, and operations administration inside of the domain the data scientist is working. This results in an individual with strong math and statistic skills, excellent communication abilities, programming and software expertise, and great business awareness.

Data scientists do not just help run software against the data, they find and pull in the data sets themselves to find out if correlations exist that can be beneficial to the organization. This process of bringing in data sets requires a data filtering process, or cleansing, to make sure the data are trustworthy.

Business skills are as critical as technical skills for data scientists (Debortoli, Müller, \& Vom Brocke, 2014). It is because of this as well as the previously mentioned skill set attributed to data scientists that has made finding individuals with all of these skills so difficult and the reason such an individual with such a broad array of skills is reeferrrêd to as a “unicornn.” When it comes to studies addressing how businesses are meeting their big data needs with such a lack of data scientists, Gupta and George (2016) reveal that there is little known about how organizations are achieving their big data capahilities.

## 经济代写|供应链管理代写供应链管理代考|数据科学家VS数据分析师

“数据科学家”是一个相对较新的头衔，它不同于传统的数据分析师角色，提供给那些发现传统商业智能系统的趋势和建模结果的人。数据分析师将使用结构化查询语言$(\mathrm{SQL})$从关系数据库中提取信息，数据科学家使用$\mathrm{SQL}$以及机器语言工具使用统计模型来查找不同变量之间的相关性(Harris, Shetterley, Alter， ＆Schnell, 2014)。SAS(2015)将数据科学家与统计学家区分开来，它解释说，数据科学家超越了描述性统计和过去结果的报告，而是对未来可能发生的事情进行预测建模。统计学家和数据分析师通常是处理类型的职位。业务领导要求提供支持性统计数据或图表，并要求从这些角色获得信息。要成为一名数据科学家，必须站在商业目标讨论的前沿和中心(Redman, 2013)

Chen, Chiang和Storey(2012)通过列出数据科学家工作领域内的会计规则、财务、管理实践、营销方法、物流方法和运营管理知识，扩展了数据科学家的独特职责。这将造就一个具有很强的数学和统计技能，优秀的沟通能力，编程和软件专业知识，以及很强的商业意识的人

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