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2017-11-03 纽约时报中文网 NYT教育频道 NYT教育频道

在技术类专业中的毕业生的人数(蓝色,图为2015至2016年间毕业的本科、硕士和博士)往往会超过工作职缺(粉色,2014至2024年年化预测值),只有计算机科学专业例外。Bureau of Labor Statistics, National Center for Education Statistics

The national priority in education can be summed up in a four-letter acronym: STEM. And that’s understandable. A country’s proficiency in science, technology, engineering and mathematics is vital in generating economic growth, advancing scientific innovation and creating good jobs.


The STEM campaign has been underway for years, championed by policymakers across the ideological spectrum, embraced in schools everywhere and by organizations ranging from the YWCA to the Boy Scouts. By now, the term — first popularized and promoted by the National Science Foundation — is used as a descriptive identifier. “She’s a STEM,” usually meant as a compliment, suggests someone who has a leg up in the college admissions sweepstakes.

STEM运动已开展多年,得到了秉持各种意识形态的政策制定者的支持,受到了各个地方的学校以及从基督教女青年会(YWCA)到男童子军(Boy Scouts)等诸多组织的欢迎。目前,这个由国家科学基金会(National Science Foundation)率先提倡和推广的概念,正被当成描述性识别符来使用。“她是一个STEM”,通常有恭维之意,指的是在大学入学资格大抽奖中具有优势的人。

Much of the public enthusiasm for STEM education rests on the assumption that these fields are rich in job opportunity. Some are, some aren’t. STEM is an expansive category, spanning many disciplines and occupations, from software engineers and data scientists to geologists, astronomers and physicists.


What recent studies have made increasingly apparent is that the greatest number of high-paying STEM jobs are in the “T” (specifically, computing).


Earlier this year, Glassdoor, a jobs listing website, ranked the median base salary of workers in their first five years of employment by undergraduate major. Computer science topped the list ($70,000), followed by electrical engineering ($68,438). Biochemistry ($46,406) and biotechnology ($48,442) were among the lowest paying majors in the study, which also confirmed that women are generally underrepresented in STEM majors.


“There is a huge divide between the computing technology roles and the traditional sciences,” said Andrew Chamberlain, Glassdoor’s chief economist.

“计算机技术的地位与传统科学大相径庭,”Glassdoor的首席经济学家安德鲁·张伯伦(Andrew Chamberlain)说。

At LinkedIn, researchers identified the skills most in demand. The top 10 last year were all computer skills, including expertise in cloud computing, data mining and statistical analysis, and writing smartphone applications.


In a recent analysis, Edward Lazowska, a professor of computer science at the University of Washington, focused on the Bureau of Labor Statistics employment forecasts in STEM categories. In the decade ending in 2024, 73 percent of STEM job growth will be in computer occupations, but only 3 percent will be in the physical sciences and 3 percent in the life sciences.

在近期的分析中,华盛顿大学(University of Washington)的计算机科学教授爱德华·拉佐夫斯卡(Edward Lazowska)关注了美国劳工统计局(Bureau of Labor Statistics)对STEM领域的劳动用工预测。在截至2024年的10年间,新增的STEM工作岗位有73%会是计算机类职位,而物理科学和生命科学类职位分别只占3%。

A working grasp of the principles of science and math should be essential knowledge for all Americans, said Michael S. Teitelbaum, an expert on science education and policy. But he believes that STEM advocates, often executives and lobbyists for technology companies, do a disservice when they raise the alarm that America is facing a worrying shortfall of STEM workers, based on shortages in a relative handful of fast-growing fields like data analytics, artificial intelligence, cloud computing and computer security.

科学教育与政策专家迈克尔·S·泰特鲍姆(Michael S. Teitelbaum)表示,所有美国人都应该掌握起码的科学和数学原理。但他认为,一些STEM的拥护者——通常是科技公司的高管和说客——发出的警告是有害处的,他们说美国正面临令人担忧的STEM劳动者短缺问题,然而这种警告所依据的短缺出现在相对较少的几个快速发展的领域内,比如数据分析、人工智能、云计算和计算机安全等。

“When it gets generalized to all of STEM, it’s misleading,” said Mr. Teitelbaum, a senior research associate in the Labor and Worklife Program at Harvard Law School. “We’re misleading a lot of young people.”

“将其泛化至STEM涉及的所有领域,会造成误导,”身为哈佛法学院(Harvard Law School)劳动和工作生活项目(Labor and Worklife Program)高级研究员的泰特鲍姆说。“我们在误导大批年轻人。”

Unemployment rates for STEM majors may be low, but not all of those with undergraduate degrees end up in their field of study — only 13 percent in life sciences and 17 percent in physical sciences, according to a 2013 National Science Foundation survey. Computer science is the only STEM field where more than half of graduates are employed in their field.

STEM专业的失业率或许很低,但并非所有本科毕业生最终都能找到与专业对口的工作:美国国家科学基金会(National Science Foundation)2013年的一项调查显示,生命科学毕业生只有13%找到了专业对口的工作,物理学只有17%。计算机科学是STEM领域唯一实现半数以上毕业生找到对口工作的学科。

If physicists and biologists want to enjoy the boom times in the digital economy, a few specialist start-ups will train them and find them jobs as data scientists and artificial intelligence programmers.


Insight Data Science Fellows Program, which has offices in New York, Boston, Seattle and Palo Alto, Calif., began its first training program five years ago and now has 900 alumni working at companies like Facebook, LinkedIn, Airbnb, Amazon and Microsoft. Jake Klamka, a physicist who founded the program, kept hearing from Silicon Valley executives that they had considered hiring traditional scientists, but converting them to technologists seemed time-consuming and risky. So Mr. Klamka decided he would start a company to provide scientists a smoother pathway into the tech industry.

洞见数据科学人才培养项目(Insight Data Science Fellows Program)在纽约、波士顿、西雅图和加州帕洛阿托都有办事处,于五年前开启了其第一个培训项目,目前已有900名受训者进入Facebook、领英、Airbnb、亚马逊(Amazon)和微软(Microsoft)等公司工作。该项目创始人是物理学家杰克·克拉姆卡(Jake Klamka),他之前总是听硅谷的高管们说他们考虑过聘请传统科学家,但把这些人变成技术专家似乎既耗时间又有风险。于是克拉姆卡判定,他可以创办一家公司,帮助科学家更顺畅地进入科技行业。

Carlos Faham made that passage. He had an impressive academic career, with a string of grant awards and fellowships. His Ph.D. from Brown University was in dark-matter physics. After Brown, he was a postdoctoral fellow at the Lawrence Berkeley National Laboratory.

卡洛斯·法哈姆(Carlos Faham)走的就是这条路。他的学术生涯成就卓著,获得过一系列助学奖和奖学金。他在布朗大学(Brown University)获得的博士学位是暗物质物理学方向。从那里毕业后,他曾在劳伦斯伯克利国家实验室(Lawrence Berkeley National Laboratory)从事博士后研究。

Dr. Faham loved the research, but after nearly two years he was feeling the strain of that life. By then, he had spent 12 years in college, graduate school and postgraduate research. His next step would be to compete for a handful of tenure-track teaching openings across the country. For the pricey Bay Area, he wasn’t making enough. A postdoc researcher typically makes $40,000 to $60,000 a year.


Dr. Faham had done serious programming for his physics research. He applied to tech companies, figuring they would be eager to hire someone with his intellectual firepower. He couldn’t get an in-person interview. He was told his background was too academic. He fumbled a couple of phone screening interviews because the statistical and machine-learning problems were unfamiliar to him.


“It was like hitting a wall running at full speed, really humbling,” he recalled.


Dr. Faham joined the seven-week Insight Data Science Fellows program in 2015. There was no formal course work. Other than a few tutorials by industry people, the time was spent creating a product — his was software for recognizing and tracking faces in video — and training for interviews. That involved solving a programming problem on a white board and explaining his thinking. “Interviewing is a muscle and you have to exercise it again and again,” he said. After the program, he received six job offers. He accepted the offer from LinkedIn. (Insight is free for participants; hiring companies pay an undisclosed fee.)


Today, Dr. Faham, 33, is a senior data scientist, working on a team that uses machine learning and statistical models to detect illicit activity on the social network, including fake job listings, ad fraud, spam and bot attacks.


The range of data-intensive detective work, he said, is “extremely rich” and “it moves so much faster than my previous world.” He makes a “pretty good six-figure salary,” about five times what he did as a postdoctoral researcher.


About 90 percent of those who enter the Insight program have landed jobs as data analysts, the company says, with a dropout rate of about 3 percent.


Anasuya Das made a similar career move, but not one as far from her academic training. After the program, Dr. Das, whose Ph.D. is in neuroscience, joined the Memorial Sloan Kettering Cancer Center in New York, where she is now a senior data scientist. She works on a team that creates software tools for the center’s doctors, nurses and researchers. One current project is a program to recommend the most promising clinical trials for individual cancer patients, based on their medical histories, age, gender and genetics.

阿纳苏亚·达斯(Anasuya Das)也做了类似的职业转变,但并没有过多偏离自己的专业背景。项目结束后,拥有神经科学博士学位的达斯加入了纽约的纪念斯隆·凯特林癌症中心(Memorial Sloan Kettering Cancer Center),现在她是那里的高级数据科学家。她所在的团队为该中心的医生、护士和研究人员创建软件工具。目前的一个项目是根据每位癌症患者的病史、年龄、性别和基因,推荐最可能有效的临床试验。

Data science is distinctly different from neuroscience, Dr. Das said, but some of the tools she employs, like a machine-learning technique called artificial neural networks, do take their inspiration from the brain. Her experience points to the larger trend that digital technologies like data science and artificial intelligence are increasingly being used in nearly every discipline. So technology and the other STEM fields merge.


That is the thinking behind a new division of data sciences at the University of California, Berkeley, that started in July. The division is a response to student demand and advancing technology. Berkeley’s “Foundations of Data Science” course attracted 1,200 students from more than 50 majors in the last academic year.

加州大学伯克利分校(University of California, Berkeley)正是出于这方面的考虑,创设了全新的数据科学部。它是为回应学生需求和技术进步而设立的。伯克利分校的“数据科学基础”课程在上个学年吸引了50多个专业的1200名学生。

The choice of the term “division” rather than “institute,” explained David Culler, the interim dean for data sciences, underlines its approach. “We want this to be something foundational across the university, innovating with other disciplines, not differentiating from them,” he said. “This is the academic world mirroring what is happening in the larger economy.”

暂任数据科学部主任的戴维·考勒(David Culler)解释说,称它为“部”(division)而非“学院”是为了强调它的思维。“我们希望它是整个大学的基础性部门,与其他学科共同创新,而不是与它们区别开来,”他说,“这是整体经济动向在学术界的反映。”




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