学什么专业？挣多少钱？——美国各级毕业生2006薪水调查

摘自八阕。

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八阕】美国大学与雇主协会，每年按4个季度分别发布新毕业的大学生、 硕士和博士研究生的薪水调查报告，这报告共涉及80个学士专业，40个硕士专业，23个博士专业。冬、春、夏三季发布的数据是各季度的数据，秋季版发布的 是去年8月31日到今年9月1日为止的整个学年数据。以下列出的是该协会最新公布的美国博士和硕士毕业生2006年秋季薪水调查报告。

美国博士毕业生2006年薪水最高的几种专业：

1、商业管理(Business Administration/Management)：$81,438

2、电气/电子与通信工程(Electrical/Electronics & Communications Engineering)： $81,297

3、冶金工程(含陶瓷科学与工程)(Metallurgical Engineering )：$80,667

4、计算机科学(Computer Sciences)：$76,630

5、化学工程(Chemical Engineering)：$75,659

6、计算机工程(Computer Engineering)：$74,750

7、工业/制造工程(Industrial/Manufacturing Engineering)：$73,292

8、物理(Physics)：$72,357

如果这些工程师没有升迁到高层管理，做到退休一般年薪不会超过$150,000

美国博士毕业生2006年薪水最低的几种专业：

1、心理学(Psychology)：$49,374

2、人文科学(Humanities)：$48,938

3、社会科学(Social Sciences)：$48,487

4、生物/生命科学(Biological Sciences/Life Sciences)：$43,916

5、英语语言与文学(English Language & Literature/Letters )：$41,405

美国硕士毕业生2006年薪水最高的10种专业：

01、计算机科学(Computer Sciences)：$71,165

02、石油工程(Petroleum Engineering)：$68,833

03、电气/电子与通信工程(Electrical/Electronics & Communications Engineering)：$66,687

04、计算机工程(Computer Engineering)：$66,545

05、地质及相关科学(Geological & Related Sciences)：$64,111

06、材料科学(Materials Sciences)：$63,500

07、航天/航空/宇航工程(Aerospace/Aeronautical/Astronautical Engineering)：$62,811

08、工业/制造工程(Industrial/Manufacturing Engineering )：$61,273

09、机械工程(Mechanical Engineering)：$61,234

10、化学工程(Chemical Engineering)：$59,008

如果这些工程师没有升迁到高层管理，做到退休一般年薪不会超过$120,000

2004年美国律师的年薪平均为：$94,930

最低年薪：$64,620，最高年薪： $143,620

在公司和企业管理层谋职(Management of companies and enterprises)：$126,250

在联邦政府谋职(Federal Government)：$108,090

在法律机构谋职(Legal services)：$99,580

在地方政府谋职(Local government)：$73,410

在州政府谋职(State government)：$70,280

2004年美国各科医生的年薪：

麻醉科(Anesthesiology)：$306,964

外科、普通外科(Surgery, general)：$255,438

妇产科(Obstetrics/Gynecology：$233,061

内科(Internal medicine)：$155,530

小儿/青少年科(Pediatrics/Adolescent medicine)：$152,690

精神科(Psychiatry)：$163,144

家庭全科(Family Practice)：$150,267

联邦人口普查局2006年11月还发表了一项调查数据“2004年美国学历与平均年薪”：

高中以下学历平均年薪：$19,169

高中毕业平均年薪：　　$28,645

大学毕业平均年薪：　　$51,554

硕士以上学位平均年薪：$78,093

从以上各项的调查报告看来，华人根深蒂固的教育观念“学好数理化，走遍天下都不怕”，似乎真的放之四海而皆准了。并且，华人父母努力说服自己的子女当律师和医生也是有根有据的。当然，学历越高收入越多的说法更是真理。

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我的评论：

• 选择专业前先要做好各个专业的benchmark, 什么专业工资最高。看来benchmark真是无时无地不重要。
• 计算机科学博士起薪只比计算机科学硕士高7.7%。虽然理论上博士比硕士有更好的成长空间，究竟读不读博士还是要慎重考虑。
• 医生收入压倒性的高，远高于其他专业，甚至包括律师。还是学医收入高。
• 读书有用啊。读书越多，收入越高。

BTW, I read this from Ian Foster's blog:

"Indeed, it was outsiders—those with expertise at the periphery of a problem's field—who were most likely to find answers and do so quickly."

Google File System

The Google File System is a scalable distributed file system for large distributed data-intensive applications, being used within Google. The paper, published in the 19th ACM Symposium on Operating Systems Principles, is a well written computer engineering paper. System builders should read it.

Quoted from its conclusion:

"We started by reexamining traditional file system assumptions in light of our current and anticipated application workloads and technological environment. Our observations have led to radically different points in the design space. We treat component failures as the norm rather than the exception, optimize for huge files that are mostly appended to (perhaps concurrently) and then read (usually sequentially), and both extend and relax the standard file system interface to improve the overall system."

Lesson learnt:

• Application driven.
• Base on the technological environment. Base on the facts of performance characteristics. Performance benchmark is the foundation for system design.
  
• Application/file system (or OS, or underlying infrastructure, etc.) co-design.
• Optimize for what should be optimized.
  

"Our system provides fault tolerance ..."

Lesson learnt:

• Reliability is a must for any system. System must work. Never forget reliability, persistence, fault tolerance during system design.
  

"... This makes possible a simple, centralized master that does not become a bottleneck. ..."

Lesson learnt:

• Down to earth.
• ...I had already come to the conclusion that in the practise of computing, where we have so much latitude for making a mess of it, mathematical elegance is not a dispensible luxury, but a matter of life and death. - Edsger Wybe Dijkstra ["My hopes of computing science" (EWD 709)]

elegant: [...]ingeniously simple and effective. - Concise Oxford Dictionary

This elegance makes perfect sense in the domain of system design. May we say in system design, elegance is not a dispensible luxury, but a matter of life and death.

Provenance of Life

We are moving. Our current landlord claims the carpet is "new". Although it does not look like a new one at all, and I remember it was not very clean when I moved in one year ago, we do not have any evidence to support our point. The landlord also blames us that we did not report problems to them promptly. Having consulted the university accommodation officer, we now learn that we had better write to them and keep a photocopy of the letter.

The evidence matters. The evidence could be pictures, receipts, letters, or anything concrete, not transient, but retrievable. Furthermore, the evidences should not be isolated. They should be logically linked together, and finally lead to some conclusion. Put it in another way, when we see an event in the life, we would like to "see" the complete process that leads to this event. By "see" we really mean to reconstruct it in a convincible way.

My boss is investigating a "provenance" project, which defines "the provenance of a piece of data is the process that led to that piece of data". The aim of the project is to "to conceive a computer-based representation of provenance that allows us to perform useful analysis and reasoning ...".

I argue we also need provenance support in our lives. Important facts should be documented in a retrievable and searchable way, for instance, in a computer-based way. We should keep recording provenance of life.

Google advocates searching instead of organizing. Ideally, as long as we record the provenance of life in our computer or on the internet, there should be a way to query and retrieve it.

I am interested in all techniques to improve productivity. It is great that some software could help to record the provenance of life an query over it, which will definitely improve our productivity of life.

The Reasons to Have a Blog

According to Wikipedia, the term "blog" is a contraction of "web log". From its original name, blog can be inferred to have two characteristics that correspond to the two purposes for me to blog.

• Blog is a log that is organized as a chronicle. I can use blog to track events in my life and to record my thoughts. In this sense, the blog is just like a diary.

• Blog is web based thus it is intended to be read by people. Blog can be a good communication tool to present the blogger. Blog is actually a bilateral communication tool because it allows other to comment.
  

Monitor the Blog and Benchmark the Program

I am using Google Analytics to monitor visits to my blog. See the picture, in which the dots indicates where somebody has visited my blog. There is even one returning visitor. Not bad!

It is a good practice to start monitoring since the website is established. Just like it is a good practice to start benchmarking since the program is prototyped.

Monitoring is an inherent requirement of a website. Thus it should become a part of website infrastructure. I.e., when you establish a website, your website is automatically being monitored. No webmaster effort is involved.

Just like benchmarking is an inherent requirement of a program. Benchmarking should become a part of development environment. When you prototype a program, it costs you zero or little effort to benchmark the program. Some benchmark specific code should be automatically added into your program. Thus according to the benchmark data, you can make a decision on whether to take a certain refactoring or not. Will Aspect-Oriented Programming help on this?