Purpose

The lack of clarity in defining data science is problematic in both academia and industry because the former has a need for clarity to establish curriculum guidelines in their work to prepare future professionals, and the latter has a need for information to establish clear job description guidelines to recruit professionals. This lack of clarity has resulted in job descriptions with significant overlap among different related professional groups. This study examines the industry view of five professions: statistical analysts (SAs), big data analytics professionals (BDAs), data scientists (DSs), data analysts (DAs) and business analytics professionals (BAs). The study compares the five fields with the unified backdrop of their common semantic dimensions and examines their recent dynamics.

Design/methodology/approach

1,200 job descriptions for the five Big Data professions (SA, DS, BDA, DA and BA) were pulled from the Monster website at four points in time, and a document library was created. The collected job qualification records were analyzed using the text analytic method of Latent Semantic Analysis (LSAs), which extract topics based on observed text usage patterns.

Findings

The findings indicated a good alignment between the industry view and the academic view of data science as a blend of statistical and programming skills. This industry view remained relatively stable during the 4 years of our study period.

Originality/value

This research paper builds upon a long tradition of related studies and commentaries. Rather than relying on subjective expertise, this study examined the job market and used text analytics to discern a space of skill and qualification dimensions from job announcements related to five big data professions.

中文翻译：

---

数据科学和大数据专业的工作资格研究

目的

数据科学定义不明确在学术界和工业界都存在问题，因为前者需要明确在其工作中建立课程指南以准备未来的专业人士，而后者需要信息来建立明确的职位描述指南招聘专业人士。这种缺乏明确性导致不同相关专业群体之间的工作描述存在显着重叠。本研究考察了五个职业的行业观点：统计分析师 (SA)、大数据分析专业人员 (BDA)、数据科学家 (DS)、数据分析师 (DA) 和业务分析专业人员 (BA)。该研究将这五个领域与它们共同语义维度的统一背景进行了比较，并检查了它们最近的动态。

设计/方法/方法

在四个时间点从 Monster 网站上提取了五个大数据专业（SA、DS、BDA、DA 和 BA）的 1,200 个职位描述，并创建了一个文档库。使用潜在语义分析（LSA）的文本分析方法对收集的工作资格记录进行分析，该方法根据观察到的文本使用模式提取主题。

发现

研究结果表明，行业观点与数据科学的学术观点之间存在良好的一致性，将数据科学作为统计和编程技能的结合。在我们研究期间的 4 年中，这种行业观点保持相对稳定。

原创性/价值

本研究论文建立在相关研究和评论的悠久传统之上。本研究不依赖主观专业知识，而是检查了就业市场，并使用文本分析从与五个大数据专业相关的工作公告中辨别出技能和资格维度的空间。