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Last active June 25, 2024 14:19
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CMU ECE Courses Career Track
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CMU_ECE_CoursesCareerTrack.md

Motivation

Career Path Code

Career Role/Tech field 1. Signal & System 2. Embedded system 3. VLSI/IC design 4. Robotics/AI
A. Professor(Academic Research) A1 A2 A3 A4
B. Industrial research(Company/national labs) B1 B2 B3 B4
C. Startup C1 C2 C3 C4
D. Manager D1 D2 D3 D4

Notation

  1. A*: A1, A2, A3, A4
  2. *4: A4, B4, C4, D4

Comments

  1. Professors
  2. Researchers
  3. Startup: tech startups are not small businesses.
  4. Mananger roles in large coprorations have significate difference in terms of qualification. General managers are mainly focusing on people, but also required to understand the target market. Product manager and project manager roles connect tech and people. Some organizations require ECE/technical backgrouds, others don't have the requirements.
  5. Other path includes patent lawyer, investment bank, Quant in banks, tech sales, presale engineers, etc.

Career transfer

``Can I change from A to B later'':

Certainly! In ECE, successful individuals often transition between technical fields, particularly once they reach senior positions. You will possess unique value and a greater chance for success if you can connect the dots from your past experiences [as Steve Jobs emphasized]. However, it's important to be mindful of human nature: we all have inherent limitations. For instance, we cannot outrun trains. While opportunities may be boundless, our biological constraints are not. Professional basketball players typically need to be quite tall, researchers often require a high IQ, and sales roles demand a strong emotional intelligence (EQ). Some career transfers are easier in one direction than the reverse direction.

``What can I try''

There are some career path much easier in one direction than other direction. For example, Einstein can play violin, but Paganini does not study theoretical physics. No professional violinst has won Nobel prize in human history. There are some psychology reasons, our brain needs to "grow" [cite Nature paper later] in order learn a skill. Many skills have a proper or ideal time window for human being to grasp. Before and after that, it is much more difficult and maybe impossive for 99% people to learn the skill.

  • Language: speak and understand subtle meanings within lanague. The golden age for spoken lanugage study is 1~3 years old.
  • Personality: the personality is usually shaped around 3~5 year old and stable fore the entire life, unless come across significant events, such as, near death experience, lost family members, etc.
  • Value system: Value system is the belived priorities of things in life, such as family vs job, believed importance of money vs happiness. Most people develop value system from high school to early 20's. It may stable through one's entir life but it is easier to change than personality.
  • Math (logic deduction / understand abstract concepts): We can remember math tricks at any ago, however, if you don't enjoy math thinking in 20's, you may like math for the rest of your life, and would not be good on math. It is diffculty to learn math proof after college.
  • Programming/software engineer: no age limitations to learn any language. However, there are limitations on these: (1) top IT companies appreciate young graduates. (2) The path to CS research demand math thinking, which has the age window.
  • Engineering (logical & follow processes / organized, want to build cool things):
  • Engineering research (passion in one tech field / curiosity, seek to influence others' mind):
  • Biology research (summarization+memory / find nonobvious truth from facts, curisty): no age limitation.
  • Sales (EQ+resilience / pay attention to people's inner requirement, ambition for money): no age limitation.
  • General management (passion+EQ / motivate people to do the right things, ambition for team): there is no age limitation on learning management skills. The personality and value system is matured around 20's.

Summary If you are not sure what career path is suitable, try different roles along with the arrow sign. If you not sure what to do, it is safer to start from the left side of the arrow. It is easier to change along the direcion of the arrow. If it is too complex, the common career changing path is

  1. Pure math -> differential equations -> signal & system -> professor / researcher -> engineer | quant -> manager | sales | laywer | startup CTO
  2. Pure math -> linear algebra -> AI / ML -> professor / researcher -> AI engineer -> presale / solution / project manager / product manager -> general manager -> startup CTO/CEO
  3. Pure math -> discrete math / algorithm -> professor / researcher -> Software engineer -> architect
  • The arrow (->) symbolizes a common shift, not a prerequisite. It is more common for a professor to join a company than for the reverse to occur. However, if you are certain you don't like the professor job, don't waste time to work on it and shift to industry later. Smiliarly, startup CEOs are not required to have a strong background in mathematics. The above diagram read this way: Occasionally, mathematicians do found companies [citing Steve Wolfram as an example], but I am not aware of any instances where a student with a pure MBA degree has excelled in mathematics, such as winning a Fields Medal.

Leveraging Skills

  • Math: when facing challenging engineering problem, math is an important tool to borrow smart concepts from other peoples' mind.
    • Differential equations: required for physical machine designs (robots, cars etc)
    • Linear algebra: required for AI research.
    • Discrete math: optional for software developers, need to pass Leet Code interview, not math proof. Required for CS research: math proof.

ECE Core

CS Core

Math / Science Reqs

Electives

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