CyberShot said:I'm wondering if I'll be able to find a job once I graduate, if my grad school plans don't pan out. I'm graduating with a BA in physics from a University of California, to be able to take computer science classes on the side (enough for a minor). Am I qualified enough to get software engineering jobs?
CyberShot said:I'm wondering if I'll be able to find a job once I graduate, if my grad school plans don't pan out. I'm graduating with a BA in physics from a University of California, to be able to take computer science classes on the side (enough for a minor). Am I qualified enough to get software engineering jobs?
Will preference be given toward actual computer science majors? Will the fact that I have a BA instead of BS hurt me?
CyberShot said:That's strange. I've always heard from professors that physicists are usually the best programmers out there.
Nevertheless, would any of you guys recommend software engineering as a short-term career (3-4 years) before getting back to grad school? How's the lifestyle?
CyberShot said:That's strange. I've always heard from professors that physicists are usually the best programmers out there.
Nevertheless, would any of you guys recommend software engineering as a short-term career (3-4 years) before getting back to grad school? How's the lifestyle?
AVReidy said:It's kind of insulting how physics majors think learning physics and a bit of programming makes them qualified for a good programming job. If you majored in physics, do something with physics. Don't think you can just grab a job as a programmer to make more money than a physics teacher would.
--AVReidyIt's kind of insulting how physics majors think learning physics and a bit of programming makes them qualified for a good programming job.
--chiroI can tell you from experience that programming is not something that you take a few classes in. You need to really breathe it, pretty much like anyone that wants to become good at it.
Insufficient for what? Can he get a job? Absolutely!FourierFaux said:--AVReidy
You're absolutely correct. Learning physics and doing a bit of programming is insufficient.
--chiro
FourierFaux said:Here's some encouragement: http://www.codinghorror.com/blog/2007/02/why-cant-programmers-program.html
That HAS to be exaggeration. I fully agree with you that it shouldn't take much more than 30 seconds for writing up the solution to that problem, and I really doubt the majority of comp sci majors would fail in doing so. You don't even need to know modular math for this, you just need to know that "divisible by" means that when you divide by that number, the remainder is zero. That's lower year elementary school maths.Astro_Dude said:That's terrifying. I wrote a solution to that FizzBuzz problem in literally 30 seconds.
Then again, I guess if you don't know modular math, as I suspect most CSCI majors don't, than it would be quite difficult.
--DaveC426913Insufficient for what? Can he get a job? Absolutely!
#include <stdio.h>
main()
{
int i;
for(i=1;i<101;i++)
{
if(i%3==0)
{
printf("Fizz \n");
}
else if(i%5==0)
{
printf("FizzBuzz \n");
}
else
{
printf("%i \n",i);
}
}
}FourierFaux said:--DaveC426913
What I meant was that it's a question of how much you actually know. Just being able to write a toy problem here and there shouldn't be enough to get a job. (Is this wrong?)
Solution to proposed FizzBuzz in c:
-------------------------
-------------------------Code:#include <stdio.h> main() { int i; for(i=1;i<101;i++) { if(i%3==0) { printf("Fizz \n"); } else if(i%5==0) { printf("FizzBuzz \n"); } else { printf("%i \n",i); } } }
:) :) :)
Pythagorean said:My undergrad was in physics, my grad study is in computational neuroscience. A lot of my research training was in signal analysis processing, programming with Matlab (computational physics). I've also taken java classes and lately become interested in Python in my spare time.
But here's the thing. I hate linux. I hate how much trouble I have to go through to install flash or install my wireless drivers (acer travelmates have a hardware button to enable the wireless, it's tough to find the drivers for this button, and when I do, they're not easy to implement.)
On the other hand, I'm currently writing a MATLAB GUI for a biology group so they can analyze their data more intuitively and efficiently. I have to consider all the user slip-ups that can happen, while transforming their thoughts into actions.
I kind of want to learn C++ now.
So, all you pretentious computer scientists out there, what's a basic program challenge (or set of challenges) that's a good test of a programmer's ability?
Pythagorean said:My undergrad was in physics, my grad study is in computational neuroscience. A lot of my research training was in signal analysis processing, programming with Matlab (computational physics). I've also taken java classes and lately become interested in Python in my spare time.
But here's the thing. I hate linux. I hate how much trouble I have to go through to install flash or install my wireless drivers (acer travelmates have a hardware button to enable the wireless, it's tough to find the drivers for this button, and when I do, they're not easy to implement.)
On the other hand, I'm currently writing a MATLAB GUI for a biology group so they can analyze their data more intuitively and efficiently. I have to consider all the user slip-ups that can happen, while transforming their thoughts into actions.
I kind of want to learn C++ now.
So, all you pretentious computer scientists out there, what's a basic program challenge (or set of challenges) that's a good test of a programmer's ability?
--CyberShotExcept that's wrong. You're supposed to print "Buzz" if i%5 == 0. and "FizzBuzz" in i%3==0 && i%5 == 0 else if statement. Finally, in the else statement there should be a printf(%i)
#include <stdio.h>
main()
{
int i;
for(i=1;i<101;i++)
{
if(i%3==0)
{
printf("Fizz \n");
}
else if(i%5==0)
{
printf("Buzz \n");
}
else if(i%3==0 && i%5==0)
{
printf("FizzBuzz \n");
}
else
{
printf("%i \n",i);
}
}
}FourierFaux said:--CyberShot
Heh, you're right. I didn't read those last two sentences correctly. Not one of my more impressive moments. :P
Code:#include <stdio.h> main() { int i; for(i=1;i<101;i++) { if(i%3==0) { printf("Fizz \n"); } else if(i%5==0) { printf("Buzz \n"); } else if(i%3==0 && i%5==0) { printf("FizzBuzz \n"); } else { printf("%i \n",i); } } }
Compiling wouldn't help you here, only actually running the program would.FourierFaux said:So switching the order of the first and the third clauses in the statement will fix that. But it's too late to impress the interviewer. That's a problem with writing something without compiling it first.
FourierFaux said:--CyberShot
Heh, you're right. I didn't read those last two sentences correctly. Not one of my more impressive moments. :P
Code:#include <stdio.h> main() { int i; for(i=1;i<101;i++) { if(i%3==0){ if(i%5==0) { printf("FizzBuzz \n"); } else { printf("Fizz" \n) } } if(i%5 ==0){ printf("Buzz"); } else { printf("%i \n",i); } } }
Pythagorean said:My Domain is scientific programming still, the only other thing I'd ever program is a video game (which is an unrealistic dream at this point) so my domains are likely:
I used to do this and it took me long hours of personal and group development to be useful in a short time frame.
-data streaming (and analysis)
-visualization of data
-networking (being able to share data and analysis results in a standard way with the community).
-but most important to me: making programs intuitive, the user interface.
CyberShot said:1.) Are comp. neuroscience phd programs part of the Neuroscience department or Computer Science/Artificial Intelligence? do you feel as if you're in a grad biology or computer science program?
2.) Is part of your research considered artificial intelligence? Or is it more geared toward biology of the brain? Also, is there a lot of math/statistics involved? I would assume a lot of probability or maybe even combinatorics? (possible different neural pathways/etc)
3.) Do you really have to do a lot of programming? A lot about neural nets I assume? Do you get to actually program "autonomous machines?" Is most of your time spent on computers doing simulations or more in a chemistry lab type environment?
4.) How many programs offer the comp. neuroscience PhD? Are they very hard to get into? What are the prestigious ones? Do you really love your research?
5.) And lastly, did you have to take classes on the side as well as your physics major? Surely, just physics courses wouldn't cut it for a comp. neuroscience program, right? What about programming pre'reqs? Or maybe chemistry/orgo/bio?
Appreciate it.
chiro said:In terms of visualization of data, I am assuming you mean things like two and three dimensional plots of some sort and the ability to transform these in ways of viewing angle, scaling, and so on. I will also add physical modelling of geometric data just to be safe.
In terms of understanding this at a deep level, its best to start with the basic of 3d modelling with areas of linear transforms and using those to create a framework with local models, world space, and camera models. You'll find nowadays that most of this is done with a few API calls to OpenGL or DirectX, whereas you had to do everything (and I mean literally everything) using super fast assembler routines or using bare bone C.
If you are dealing with a relatively small amount of data, it's probably useful to just use your graphics card z-buffer. If the geometry is really large, then you need to study spatial classification algorithms and how they are used in hidden surface removal.
For standard modelling where there are no fancy textures or lighting, you will able to set up the video card (again with OpenGL or DirectX) pretty quickly.
With regard to data analysis and data streaming, this will obvious depend on the type and the source of the data, which will dictate the tools that you will use. For example if its some kind of ODBC data source, then you will get an API and libraries for this so you can effectively import the data and then convert it to your data structure for processing.
In terms of streaming, between components of your program, if data is shared specifically in the process space, it will be a lot easier. If your data though is being shared between processes, then you will have to learn about objects like pipes where processes can share public data with each other.
In terms of networking, it's a good idea to see what application protocols are out there, because chances are the protocol's development had the same goal in mind as yourself. It may not be specifically written in mind for your domain, but chances are a lot of the ideas are directly related. As an example think about peer 2 peer software. When most people think of p2p they think of file sharing, but p2p is also used in video broadcast servers (I'm using one for a distance course on Bayesian Inference). So in this example you have the framework for p2p, and then the specific customization for the application you have in mind.
With regards to that comment I would look at the basics of a good p2p protocol and then think about the data structures you need in your application and use both of those as well as specific protocols that you need as a basis for designing a network platform for your application.
More specifically about the science, I'm interested in complex systems and nonlinear dynamics, which means i also have to be very away of floating point errors (since they are dramaticized in chaotic systems).
As for intuitive interfaces, that is a real ***** of a subject. For anything involving navigation through visualizations (in other words through a 3D virtual world or representation of some sort), you will need both a visualization system that has very specific control over the rendering and the detection of objects in the world. Typically we use the term "hit test" for the ability to cast a ray and return the object that was first hit.
Coding the basics for that requires collision detection. If you don't have many objects, then you could get away with something simpler, but if the complexity of your visualization grows, then you will need something more versatile.
One integrated way for interactive visualization is based on what games like Doom 3 do: they dynamically create a texture (think bitmap) and then they render that to some object. So in other words you create a flat quadrilateral with two triangles, then you create your output texture by computing it based on the representation and events executed by the user (user moves mouse, clicks on button, etc), and then using the output texture as the texture for your quadrilateral.
In the above scenario, the whole system is unified under a general 3D visualization engine. With this you could have many 2D interfaces within a 3D virtual world and you could give the interfaces all the fancy stuff that you see in your state of the art PC games because everything goes through the same pipeline, so you get everything from transparency to texture effects for free.
But doing this takes a lot of work (and I know from experience).
If you ever want to get into video games, the key things is finding the balance between flexibility and performance and nowadays you need high amounts of both. The performance criteria means that optimization is very important and understanding this in a variety of contexts from computation time to general playability. Hardware is getting better and better, but as a consequence, people want more and more detail and fireworks which means finding solutions that scale is critical.
So with some game engine, the important point is the ability to create content quickly and use it quickly. As a result of this game engines have their own scripting languages, geometry exporters, and numerous tools to create the worlds, network protocols, user interfaces, and game specific code, which are designed to be flexible and easy to get something up and running.
It can be a nightmare in a lot of ways, but having a finished game is pretty cool.
Fra said:One extremely interesting, and I think nice for the future is modelling organism responses accounting for gene expression and other regulations. The point with complex systems is that even if it "in principle" if you look at the model, is determinisic, complexity and rounding errors prevents this from actually beeing predictive.
twofish-quant said:And then you can use the same math to model financial systems.
Fra said:In economical and social systems, some issues might even be more obvious than in physics, sometime I'm mentioned in a few threads. Smolins "sidekick" Roberto Unger who is an expert in social theory and law have actually provided several deep insights and comments on the nature of law (in the context of smolings evolution of physical law) in a joint talk at perimeter.
Certainly the abstract "modelling and inference problem" are very similar for someone looking to placet their bets in a market that is completely determined by expectations and where the notion of fundamental values are ambigous at best (here I compare to fundamental DOFs)