Wednesday, August 27, 2014

Reasons why Math/Statistics losing to ComputerScience

https://news.ycombinator.com/item?id=8228978

From one of the comments:

Here is a math major's perspective (I also have a C.S. master's from Stanford, a decent school for computer science).
The real reason statistics is losing to machine learning is M&M: money and marketing.
Money is a huge factor when kids choose majors in college. Many of them have student loans to pay off, and for many of them, getting a high-paying job post college is a serious consideration. In this regard, statistics is a great major, but computer science is flat-out ridiculous. When big tech companies are offering a Stanford graduate with only a few summers of programming internships under the belt for 150k/year, naturally a lot of kids are lured into computer science.
Then, once they start studying computer science, they discover this thing called machine learning. While I do think there is a difference in emphasis between stats and machine learning, the fundamentals are same, except the nomenclature sounds much cooler in machine learning. Nonparametric inference sounds esoteric and cryptic, but unsupervised learning sounds futuristic and cool.
The biggest problem with both statistics and machine learning education, I would say, is their lack of emphasis on mathematical foundations. When I was in college, CS229 (Introduction to Machine Learning) was touted to be the hardest class at Stanford. Having helped my friends wade through CS229 problems (a lot of which comes down to wading through linear algebra and multi-variable differential calculus), I do not think this is remotely true: CS229 is hard because it attracts students who do not have the requisite mathematical maturity to learn statistics/machine learning in a serious way (I am not even talking about the real-analytic foundation of probability and calculus but rudimentary linear algebra and chain rules).
Also, I do think CS229@Stanford is a great class that brings theory and practice together =)
As for R/Python/Matlab/etc., I will let the zealots argue what's best. To me, they are like statistics and machine learning: similar with different emphasis.
reply

1971genocide 15 hours ago | link

I agree with your points so much.
I am a applied Maths student (currently) and when looking for internships I noticed 80% of them are for CS related stuff. Imagine being me ( 19 years old ), offered a 15K/year job in my first year in university. My best job up until then was cleaning the floor at a mall. I have studied insane amount of mathematics all my life ( proofs after proofs ), how much longer do I need to wait until I am able to something of value in society ?
Compared that to the amazing 14 weeks internship I did at a startup churning out frontend javascript. Those 14 weeks were the best weeks of my life, I never felt more confident and of value. My depression vanished overnight, I found a new aim in life, I treated coding the same way nations treat nuclear weapons.
I learnt more things of value in those 14 weeks then I did in the previous 19 years, I didn't need to set up an alarm clock to wake up in the morning. There was no hand holding, because I was getting paid. I spent the rest of my waking hour churing code with no need for external motivation.
I got really angry at society for misleading me for such a long time because of how easy it was for me to do something useful, finally. I see my friends struggling with the same problems and feel so much of young ppl's time is wasted, In my mind its a crime.
I think as always mathematicians fail to grasp the human side of the argument, why are they complaining about lack of interest in statistics ? its their own fault this problem exists. There is no Neil Degrasse Tyson for statistics, there is no mark zuckerberg for stats, you don't need to hire a PR department. Just show us the wonderful magic that you speak of and we are smart enough to figure out why we should dedicate our lives to your cause.
This is why if some statistical axiom/theory doesn't have a CS applied part I became really skeptical of doing that course.

Thursday, August 21, 2014

Thursday, August 07, 2014

Timeless stuff

http://www.kalzumeus.com/2011/10/28/dont-call-yourself-a-programmer/

Focus on the company's bottom line and customer satisfaction. That is your job, not making pretty code.
To get a [good] job, you have to literally put your foot in a door; you must be connected to some person who works at that company.
The amount of money you get is based on the size of the company and your perceived worth. Ask for more than you think you're worth.
Make yourself look good. Talk up your cool projects, wax philosophical about your ideal development methodology, then hammer home how you're focused on the customer, efficiency, and the business's bottom line.
Focus on the key elements in any situation, don't get distracted by minutia, be assertive. Your motto is "restrained, confident professionalism."
"people are hilariously easy to hack" in the way you present yourself, so use all the hacks available. Be the James Bond of CRUD apps.
things not mentioned in the post:
Startups will hire you for positions you might not be qualified for and pay you peanuts for it, but it'll create experience/value/resume filler.
Academic achievement is only significant to people who prize intelligence over productivity. Just know how to get the work they need done and show them that.
Networking in person isn't necessary. Just build relationships.
If you're new, add [real] stuff to your resume to make it seem like you have relevant business experience, and be able to back up whatever you put down.
Don't "work your way up" through some corporate ladder. Just apply for the job you want. At some point you'll get it.