In case you guys are wondering about current college curriculum, I just graduated with a BS in biotechnology-bioinformatics so I can provide a little insight.
My core classes consisted mainly of biology, chemistry, organic chemistry and physics. Other classes that my degree required was advanced mathematics (with biological application), advanced statistics, and computer science.
About the computer science portion of my degree: I was required to learn C, C++, discrete math, Perl, and data structure/algorithm design. I chose to take machine language as an elective.
What I've learned in industry: The CS foundation I built in college was critical. Although Perl is widely used where I work, languages like R and C are used more often (for my particular projects). I've also learned that my job is to bridge the gap between biologists and computer scientists.
Biologists say what they want to get-> Statisticians/Mathematicians think up a procedure -> I make sure the formulas make sense with the subject at hand, program it in Perl or whatever -> CS people optimize it and do their magic to make it run super fast -> checked by everyone to make sure its okay. -> stats analyze and feedback to the biologists.
My point being, I always think everyone should learn more math, but the industry has found a way to get around everyone needing to learn everything (jack of all trades master of none) to having experts work together towards a common goal (an oceans 11 type set-up). Everyone has something special to offer. Personally, I think the current set-up is working fine. Although everyone should learn more advanced math (or biologists should learn more CS), not everyone is willing and/or capable.
My core classes consisted mainly of biology, chemistry, organic chemistry and physics. Other classes that my degree required was advanced mathematics (with biological application), advanced statistics, and computer science.
About the computer science portion of my degree: I was required to learn C, C++, discrete math, Perl, and data structure/algorithm design. I chose to take machine language as an elective.
What I've learned in industry: The CS foundation I built in college was critical. Although Perl is widely used where I work, languages like R and C are used more often (for my particular projects). I've also learned that my job is to bridge the gap between biologists and computer scientists.
Biologists say what they want to get-> Statisticians/Mathematicians think up a procedure -> I make sure the formulas make sense with the subject at hand, program it in Perl or whatever -> CS people optimize it and do their magic to make it run super fast -> checked by everyone to make sure its okay. -> stats analyze and feedback to the biologists.
My point being, I always think everyone should learn more math, but the industry has found a way to get around everyone needing to learn everything (jack of all trades master of none) to having experts work together towards a common goal (an oceans 11 type set-up). Everyone has something special to offer. Personally, I think the current set-up is working fine. Although everyone should learn more advanced math (or biologists should learn more CS), not everyone is willing and/or capable.
I hope this was helpful.