mdlthree

One could also say that agriculture changed machine learning via RA Fischer, pioneer of modern statistics - https://en.wikipedia.org/wiki/Ronald_Fisher#Rothamsted_Exper...

https://www.youtube.com/watch?v=g8UePdbDmMw This was one of the keynote speakers at the 2015 IISE National Convention. Nancy Currie was/is works for NASA and has education in Industrial Engineering. She worked extensively investigating the Columbia space shuttle incident. Her involvement shows how industrial engineers participated in making space flight safer / better. It's really not grokking the physics that can make things better. Excellent presentation.

Mechanical engineers make things, industrial engineers make things better. The discipline started in the 1900s mostly concerned with improving manufacturing efficiency and cost. Now it has grown to improve all facets of product quality and business process (physical and digital). It is one of the best disciplines to use applied math. https://en.wikipedia.org/wiki/Industrial_engineering

From my experience, I would offer the generalization that nobody hires mathematicians. Mathematics in society is more of a skill set than a professional title. The most challenging or cutting edge math that could be commonly used is the LINEST() function in Excel. A person who is good at math also has a lot of great skills to offer a company, it is just selling those features and not the calculus.

I started out as a math major, then I transitioned to a double major math AND stats because stats is more applicable. I struggled for a year looking for work (also US immigration sucks, even for Canadians) and ended up in a master degree program in Industrial Engineering. I chose engineering specifically for the word "engineering". I was lucky that I discovered the field of Industrial Engineering at that university otherwise I was headed for a BS in Mechanical.

Continuing formal math education will further limit the kind of jobs you can apply, increasing the level of competition. Even the BS in Math left me with the feeling people saw me as over qualified, lacking regular skills.

Math is super great by the way, just not the idea of being a "mathematician". It (unfairly) causes alienation of your true potential.

Video by CGP Grey on a similar topic - Humans need not apply - https://www.youtube.com/watch?v=7Pq-S557XQU

an example of this in science - http://xkcd.com/1133/

The height vs weight chart is screaming for a linear regression line.

This problem is like Project Euler #1. You don't strictly need to divide. I prefer to skip along the array and put in the new values.

  n,f,b= 100,3,5
  a = [str(i) for i in range(1,n+1)]
  for i in range(n/f):
      a[(i+1)*(f)-1] = 'Fizz'
  for i in range(n/b):
      a[(i+1)*(b)-1] = 'Buzz'
  for i in range(n/(f*b)):
      a[(i+1)*(f*b)-1] = 'FizzBuzz'

I want to bring up the fact that there is a town in Texas called Canadian [http://en.wikipedia.org/wiki/Canadian,_Texas], and that is probably where a woman reported the guy. Also a county in Oklahoma, [http://en.wikipedia.org/wiki/Canadian_County,_Oklahoma].

This story has nothing to do with Canada the country.

I think the important distinction is that he is at home in the regular environment. If you are away from home without the family, everything is different; while at home only the absence is different. This absence feels massive because of the magnitude of habits formed participating in a family unit.

I am having trouble processing some cognitive dissonance when comparing your parable of the "Crazy Egg Enterprise Edition" but then using pricing strategies to take advantage of the sane kind of managers. While, I understand the concept of feature segmentation, I supposed should not be concerned with how other people run their business. I only need to focus on my profitability and not being a sucker.

I have been looking for a job in manufacturing and business operations for a few years now while finishing an MS of Industrial Engineering. If I was only allowed 1 response from the prospective employer, it would be the anonymous credentials of the man/woman who did get the job.

There is a learning opportunity for me to align my expectations and my credentials to the jobs I applied to. If I could identify that major road block, I could fix it. I could apply to fewer positions knowing what was appropriate or not. Everyone else could too and there would be less resume traffic overall.

My intention of the word introductory was not to feel like training wheels. Joeri in this thread says:

>>You have to trade off developer productivity for performance when it comes to choosing frameworks. The slower the framework the more it does for you.

And in that respect, the more the framework does for a developer, the better it is probably suited a starting point for new developers. It is a badge of success that Django and Rails can be able to serve a whole spectrum of needs.

As an amateur hacker and after 275 days of reading Hacker News I feel I can now navigate in the sea of client side Javascript frameworks. With the introduction of this data on web framework performance, I now have another set of choices that I am completely unqualified to comprehend.

My first impression is that this data shows about 3 levels of web frameworks. At the bottom (slowest) we have Django's and Rails and many other introductory app server frameworks. Lets say after I was able to build an initial product successfully, would I then consider re-building the product in a higher performance framework as Go, Node, etc?

The third level, netty and gemini and servlet etc, I am not familiar with. Googling "netty" I get --"Netty is an asynchronous event-driven network application framework"-- I thought that is what Node is (in js) and Go does with gophers.

What are the use cases for these faster frameworks and do they follow an evolution of performance options that an app might go through?

I tried LinkedIn Pro as a try free for 30 days. This required a credit card as it will bill you if you forget to cancel. After a week or so, I was unimpressed and canceled. Or I thought I did. $150 later and three months of not seeing the recurring transaction on my credit card I realized that I had not clicked the right buttons to cancel. I felt cheated by deceptive UI design.

Schrodinger's cat is just like the old school intuition that is subject to errors. It is also why physics may have a better public image than math, due to the higher availability of intuitive ideas in describing physical phenomena. We wouldn't need a simultaneously alive and dead cat if people were more educated about the idea of a random variable. Heck, five seconds on wikipedia leads to "a random variable conceptually does not have a single, fixed value (even if unknown); rather, it can take on a set of possible different values, each with an associated probability."

The Revolution:

At the turn of the 1900s, the field of mathematics was changing. The old school relied of physical intuition as a means of proof. The new school found that an adherence to formal logic led to more reliable findings.

Most Relevent Sections:

In brief, traditionalists lost the battle in the professional community but won in education. The failure of “new math” in the 1960s and 70s is taken as further confirmation that modern mathematics is unsuitable for children. This was hardly a fair test of the methodology because it was very poorly conceived, and many traditionalists were determined that it would succeed only over their dead bodies. However, the experience reinforced preexisting antagonism, and opposition is now a deeply embedded article of faith.

Many scientists and engineers depend on mathematics, but its reliability makes it transparent rather than appreciated, and they often dismiss core mathematics as meaningless formalism and obsessive-compulsive about details. This is a cultural attitude that reflects feelings of power in their domains and world views that include little else, but it is encouraged by the opposition in elementary education and philosophy.

In fact, hostility to mathematics is endemic in our culture. Imagine a conversation:

  A: What do you do?
  B: I am a ———.
  A: Oh, I hate that.

Ideally this response would be limited to such occupations as “serial killer”, “child pornographer”, and maybe “politician”, but “mathematician” seems to work. It is common enough that many of us are reluctant to identify ourselves as mathematicians. Paul Halmos is said to have told outsiders that he was in “roofing and siding”!

from page 33-34.

Core methods such as completely precise definitions (via axioms) and careful logical arguments are well known, but many educators, philosophers, physicists, engineers, and many applied mathematicians reject them as not really necessary.

from page 34

Why It Matters:

The sciences are at risk in the reckless implementation of maths.

Education methods are weakened by the philosophical divide.

I am not proud of its length, a by product of the poor vocabulary that summarizes my skills. Instead I have to list the words that are most likely to be associated with my skills. http://youtu.be/qiPc1mbRCxs

Thanks for that link. It also did remind me of one Australian company [http://www.biarri.com/about_the_team.shtml] that I explore from time to time. They seem to offer the most types of OR decision support for business, even with SaaS delivery.

As a graduating student in this field of "industrial_engineer/computer_scientist/mathematician/statistician/economist" I found this story and the rest of the site (not the front page picture) fairly inspiring. There are no role models even with the decent amount of blogging the OR/MS community does. They are still stuffy academicians, perhaps so because they could not find role models in industry either.

He answers this question fairly concisely in the first section "What I do", the link in the first section [http://www.the-adam.com/adam/math-4q6/hypersq.htm], and the second last section "What I am not".

I am finishing an MS in Industrial Engineering, a synonym of Operations Research. I have also been looking for related jobs that use those skills for two years. Many relevant jobs are in big data and analytics, subjects common to Hacker News; many belong to business operations such as demand planning and supply chain management. To find jobs I use Indeed.com and this is the search query I use. I based a term project in business intelligence on the problem of finding good search terms based on course descriptions.

[http://www.indeed.com/jobs?q=title%3A(-vp+-director+-manager...]

But I will digress from making this more personal. Basically from the stand point of one line job titles, there isn't one for the line of work he is in. The obvious ones are to frightening to the masses. Maybe it is just our culture today where "Math" is a four letter word.

Thats was a great Q/A article on fusion. That one deserves sticky.

I tried past cover letters for jobs and they all scored in the 0.5-0.6 range. This makes sense since I AM trying to sell myself in an honest but tailored way. Does this reveal a flaw in my writing style or in the purpose of cover letters in the hiring process? Is it even possible for a cover letter to score low on a bullshit scale?

What do providers get for access to these type of files? Can they capitalize on the information gathered from file requests? Adding to their knowledge of traffic patterns etc...

My background: Double major in Mathematics and Statistics. I am currently studying for a masters in Industrial Engineering BECAUSE my math/stats degree could not secure me a job after looking for 1.5 years. People don't see the value in math and you can't make them. That's why I am going to trick them by getting a degree with "engineer" in the title and maybe I can finally make it past the HR robot resume scanners.

1.The course content looks ok. There is always some sort of crap class that the department "thinks" you should take. For you it is Mat 380 Error-correction Codes. Out of the classes you get to pick, you should absolutely consider "QSO 320-Intro to Management Science". Mathematics departments (I have been through about 4 different ones) do a terrible job of application. None of your classes except QSO 320 will have day to day usefulness.

2.If you have not paid for this degree yet I would encourage you to look up programs for the following departments. Management Science, Operations Research, Operations Management, Business Intelligence, Data Science. They usually have just enough theory to carry you but focus on problem solving methods and decision making.

3.If your future goal is to go on to a masters then to some sort of research based work, then a math degree is a great jumping point. When ever I have come into a practical application course, I crush the theory and have an advantage in the ease of absorbing the knowledge. If you are not really sure about research/MS/PHd I strongly advise against this plan. You will be disappointed in how little people understand the how to leverage math skills.

In general, school is where you pay money for the privilege of doing homework and writing tests subject to the human faults of a non-perfect professor. I would advise you to look at the course syllabi for the following Coursera classes. So much good content coming from there. At least you will be more knowledgeable of the breadth in these subjects. - https://www.coursera.org/category/cs-ai - https://www.coursera.org/category/stats - https://www.coursera.org/category/cs-theory - https://www.coursera.org/course/operations

The funny thing about that list is none of them are from the mathematics category. Math is pervasive but struggles to compete without context. Take courses with context, unless you really want to pursue an academic career.