商业聚焦数据科学的3个重要职业

  • A+

数据科学并不属于什么全新学科,但其最近却随着大数据技术的快速发展而日益得到关注。顾名思义,数据科学的主旨在对研究数据——更具体地说,用于指导如何更有效地理解、存储及操纵数据。考虑到众多企业开始意识到数据的社会与经济价值,而处理相关数据任务亦存在着巨大挑战,因此合格的数据科学家开始成为人才市场上的热门资源。

商业聚焦数据科学的3个重要职业

通常来讲,获得数据科学硕士等高级学位足以把大家送入相关职位。数据科学家能够在与大数据相关的任何领域找到工作,包括高校、医疗卫生、科研院所、政府机构等等。下面,我们一同了解其中的三项具体职业发展道路。

1. 数据科学家

人才市场招聘信息中给出的头衔通常为“数据经理”或者“统计学家”等。

无论具体名称如何,数据科学家们需要利用自己的数学及编程技能对数据进行直接处理。数据科学家们需要立足自身职位追踪贯穿项目的全部数据,构建数据存储空间并组织预测建模流程,最终将发现报告给决策者。因此,数据科学家通常需要掌握扎实的编程语言,特别是Python与SQL。

数据科学家目前的平均年薪为11万5千美元,不过入门级从业者的预期薪酬大概在8万美元左右。到2024年,市场对于数据科学家的需求将增长30%,这意味着仍有大量职位等待着后来者。

2. 数据工程师

数据工程师又被称为数据架构师或者数据库管理员,其职能与真正的数据科学家略有区别。事实上,部分数据科学家可能认为,该职位只需要普通的计算机科学学位即可胜任——当然,拥有数据科学专业背景更好。

与其他类型的工程师类似,数据工程师同样需要了解如何利用素材构建解决方案。数据工程师需要熟练掌握数学方法、编程与大数据技术,且能够娴熟地在数据集中处理包含的信息,同时清理不必要或者混乱的信息内容。

同样,数据工程师也应该拥有丰富的Python与SQL经验,而基于Java类框架(如Hadoop)相关技能亦能够让大家在工作中更加如鱼得水。

此类职位的平均入门薪酬为8万1700美元,而行业中的顶级人士能够拿到10万美元。数据工程师职位的增长速度相对较慢,到2024年增量约为11%,但仍高于整体人才市场的平均水平。

3. 数据分析师

尽管“分析师”与“科学家”这两种称为间的界定并不明确,但数据分析师明显与商业实践关联更为紧密。一般来讲,数据分析师可以顺利上手“某某分析师”类职位,包括项目分析师、市场研究分析师、信息安全分析师、商务分析师等等。

数据分析师职位负责帮助未经过数据科学训练的人员理解数据内容。通过创建有吸引力且易于理解的图形、图表或者简单描述语言,数据分析师能够顺利将信息传达给他人。除了统计相关技能,数据分析师还需要具备将数据转换为业务术语及策略的能力。另外,SQL与Excel技能同样必须掌握。

也许由于对于技术性知识的要求相对较低,因此数据分析师的平均年薪也较低,为6万5千美元。不过由于与业务更为贴近,因此分析师们更有机会在行政领域有所建树,从而将自身薪酬提升至六位数。另外,这一领域的职位数量增长率很高,到2024年就业机会将增加30%。


3 Important Careers for Business-Focused Data Scientists

Computer science might forever remain the go-to field for broad tech knowledge, but specializations are becoming more and more valued by employers around the globe. For example, cybersecurity professionals are becoming increasingly necessary for businesses to keep private information digitally and physically safe. Information systems managers are focused on keeping networks live so businesses’ employees can cooperate on profit-earning projects. However, for big and small businesses alike, one of the most fascinating and useful specializations is that of the data scientist, who is adept at extracting meaning from terabytes of seemingly useless numbers.

Data science isn’t a new discipline within the ever-expanding tech industry, but it has recently gained some popularity alongside the increasing prominence of Big Data. As the name suggests, data science is the study of data ― but more specifically, it is the effort to understand, store, and manipulate data efficiently. As companies and individuals generate more and more data ― largely because companies have begun to recognize the social and economic value of that data ― the task of organizing and using data effectively has become more challenging. As a result, qualified data scientists have become hot commodities.

Typically, earning an advanced degree, such as a data science master’s online , will qualify one for work in the field. Data scientists can find employment almost anywhere that makes use of Big Data, such as universities, health care facilities, scientific research facilities, government agencies, and more. The key is to look for jobs in any of the following three career paths.

1. Data Scientist

商业聚焦数据科学的3个重要职业

This one should be a no-brainer, but rarely do recruiters post positions for data scientists that are so clear-cut. More often, jobs in this career path have titles such as “data manager” or “ statistician ”.

Nevertheless, whatever they are called, data scientists on this track use their mathematics and programming skills to handle data directly. Usually, these positions allow data scientists to track data from one end of a project to the other ― an exciting opportunity for control ― by building data storage spaces, composing predictive modeling processes, and reporting on their findings. To do this, data scientists typically need a firm grasp of coding languages, particularly Python and SQL.

The average salary for experienced data scientists on this track is $115,000, but entry-level workers should expect to make closer to $80,000. By 2024, the need for data scientists should grow roughly 30 percent, meaning there are plenty of openings for new workers.

2. Data Engineers

商业聚焦数据科学的3个重要职业

Also called data architects or database administrators , data engineers have slightly different responsibilities from true data scientists. In fact, some data scientists argue that it is possible to excel in this career with a general computer science degree ― but that doesn’t mean a background in data science isn’t better.

Like other types of engineers, data engineers are particularly interested in working with raw materials to create solutions. Using math, programming, and a familiarity with Big Data, data engineers are almost always elbow-deep in data sets, working to process the information contained therein and to clean up unnecessary or confusing information.

Similarly, data engineers should have extensive experience with Python and SQL, but skill with Java-based frameworks like Hadoop is also a plus.

Entry-level salaries for this occupation average about $81,700, while those at the top of their field can make over $100,000. Growth in data engineering is a bit slower than other data science fields, at 11 percent by 2024, but still faster than the national average for all jobs.

3. Data Analysts

商业聚焦数据科学的3个重要职业

Though the distinctions between the terms “analyst” and “scientist” may not be clear, this career distinguishes itself by being much more closely linked with business practices than the previous paths. Typically, data analysts can comfortably fill any position with “analyst:” project analyst, market research analyst , information security analyst , business analyst, and the list goes on.

The job of data analysts is to make data comprehensible to those not trained in data science. By creating attractive, intelligible graphs and charts or using plain language, these data scientists are able to make the information come alive to everyone else. Having statistics skills is of course mandatory, but just as important are the abilities to translate the data into business terminology and strategy. Using SQL is vital but equally so is familiarity with Excel.

Perhaps because data analysts rely less on technical knowledge than other career branches in data science, the average starting salary for this career is about $65,000. However, because it is more closely related to business, analysts have more opportunities to reach executive positions, netting them salaries well into the six figures. Plus, growth is substantial in this field, with 30 percent more job openings by 2024.

2016年度中国软件开发者白皮书下载(PDF)
深入浅出数据分析(中文版)
机器学习电子书
Excel数据可视化分析方法大全

发表评论

:?: :razz: :sad: :evil: :!: :smile: :oops: :grin: :eek: :shock: :???: :cool: :lol: :mad: :twisted: :roll: :wink: :idea: :arrow: :neutral: :cry: :mrgreen: