Need help on deciding specialization within computational science

In summary, the person is considering a few different areas of specialization and wants some advice. They have narrowed it down to two and are not sure what to do. The person asks for advice on what to do and is told that the most important thing they learned in university was statistics. They may end up choosing Management Science and Engineering.
  • #1
foges
53
0
So I decided to my masters in computational science. I chose this because the part of my undergrad that I enjoyed the most was the math/programming/numerical methods. Within Computational Science I will have to specialize and I am really not sure what I want to do, therefore I was hoping to get some advice. I have narrowed it down a bit, but really want to make sure I do something where there is good job security

Stanford lists the specializations as:

-Aeronautics and Astronautics: Not my main interest, also an area which doesn't have many jobs unless you work in academia or for a big company like boeing

-Computational and Mathematical Engineering: Reasonably interesting, but I think it might be a good idea to specialize at least a bit, particularly if I decide to do a PhD

-Computer Science: Not very interested

-Electrical Engineering: Don't know how useful the quantum mechanics is for anything but research and the big semiconductor companies. I also think that electromagnetics will limit me to a couple of big companies.

-Management Science and Engineering: Sounds interesting, but what companies really use this? I also have a slight preference for harder sciences

-Mathematics: Pretty general, would rather do Computational and Mathematical Engineering in this case

-Mechanical Engineering: The whole structural and fluid/aero dynamic simulations seem really interesting, but how many jobs are there in this field?

-Statistics: Was unfortunately never my strength (not bad at it, just don't excel as much as I do in other areas)

The specific courses are listed on this website: http://icme.stanford.edu/academics/programs/ms.php

Thanks a bunch for the help guys. Also I really don't mean to offend anyone working in the areas that I have chosen not to specialize in..
 
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  • #2
Hey foges.

Just a few comments.

If for Management Science you are doing any kind of Operations Research, then yes this is extremely useful and is used for a lot of different things from packing trucks right to minimize space wastage, to making all kinds of business decisions. Very important area used across many different types of businesses in general.

For Statistics, if its hands on project based work I don't think you can go wrong. Given that its stanford, I imagine the program will be pretty good and given the kinds of companies in the area like Google that heavily utilize statistical methodology in a computational way, it's a good indication of how valuable this should end up being.

For mathematics the focus seems to be on finance since a lot of the subjects are directly applicable to that but having said that stochastic analysis applies equally to other things like the sciences and engineering. For example if you have to analyze anything with noise, you're going to have to resort to understanding stochastic processes which is pretty much everything nowadays (a sweeping generalization, but IMO still a statement that has value none-the-less).

But yeah if it doesn't capture you enough to initially interest you, you're probably better off spending your time and money somewhere else.
 
  • #3
Hi chiro, I really appreciate the input. Yes I have also heard from many people that the most important thing they learned in university was statistics. I may end up going with Management Science and Engineering, it seems like a good mixture of optimization and stats, and is reasonably geared towards operations research. It's also what I described as my main interest in my cover letter :P Just wanted to make sure I wasn't making a mistake by not going for a harder science.
 

1. What is computational science?

Computational science is an interdisciplinary field that combines computer science, mathematics, and scientific disciplines to solve real-world problems using computational methods and techniques.

2. Why is it important to specialize in a specific area within computational science?

Specializing in a specific area within computational science allows for a deeper understanding and expertise in a particular subject, which can lead to more advanced and innovative solutions to complex problems.

3. How do I decide which specialization to pursue within computational science?

It is important to consider your interests, strengths, and career goals when deciding on a specialization within computational science. Researching the different options and talking to professionals in the field can also help guide your decision.

4. What are some common specializations within computational science?

Some common specializations within computational science include data science, machine learning, bioinformatics, computational chemistry, and computational physics.

5. What skills and knowledge are necessary for a successful career in computational science?

A successful career in computational science requires a strong foundation in mathematics, computer science, and a specific area of specialization. Other important skills include problem-solving, critical thinking, and the ability to work with complex data and systems.

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