I too never thought I was smart enough - in my case, for computers. Nothing to
do with my gender. My father bought me my first computer at 10. But, others
said that I can't learn programming if I don't know maths, and I was crap at
maths. So I didn't learn to program.
Not until I went to university, to study computer science, of all things. I
was good at language, so I treated programming as a language problem. I
learned well enough to make a career for myself as a programmer. I guess that
doesn't say much, though, there's many bad programmers out there who make tons
of money.
After university, I did a masters in Data Science. Why I get myself in those
situations, I'll never know, but I studied Data Science. By then I knew how to
program so I tackled all the maths problems as programming problems. When I
didn't get something, I coded it. So I coded a bunch of linear and logistic
regressions, and perceptrons and recurrent neural nets and the like, 'till I
grokked them right.
So now I'm studying for a PhD in machine learning, in one of the best
universities in the world. Not bad for a kid who always got the worse grades
at maths, at school, eh?
So what I learned is this:
a) play to your strengths, not your weaknesses.
b) Dont' believe when people say you haven't got what it takes. They only know
one way to do things. You do you.
Oh and- you know what? Turns out I'm not that bad at maths after all.
Certainly not the discrete maths used in computer science. Not even the
continuous maths used in data science and machine learning. I get by.
>b) Dont' believe when people say you haven't got what it takes.
Maybe. To play devil's advocate, there are times one should consider that advice. Someone trying to break into performing arts or professional sports may benefit from getting some frank, realistic advice, especially if they don't have the talent or aptitude for the work.
I would also argue there is already too much of the "one should always believe in oneself" pushed by culture - a core message of pretty much every kids TV show since at least the 80s. A more relatable example would be a person investing their life savings in a terrible idea but everyone around them telling them "it's great" and "they should keep going" so as to not hurt their feelings.
Not until I went to university, to study computer science, of all things. I was good at language, so I treated programming as a language problem.
Just today I was reading this blog post from someone who loved math, went into programming, and claims that programming isn't math, it's language, and that the image comes from early programmers being mathematicians and the industry hasn't moved on from that image yet:
People with a math background did fine, of course, but people with a heavy language background often did better. I saw this curious effect again when I started working with high schoolers, with a similar curriculum. Bilingual kids often took to programming more easily than monolingual kids.
I remember this being a common belief back in the 90s.
At school I knew a guy who was good at maths, obsessed with computers (as in often knowing more than the teachers) but was rejected from the computing course (until his parents complained) because he wasn't doing well in English.
Personally, I believe they were getting confused with English as it was taught (mainly literary appreciation) and Language/Linguistics (as in the grammar/semantics and logic) which wasn't actually taught in (my) high school.
Not until I went to university, to study computer science, of all things. I was good at language, so I treated programming as a language problem. I learned well enough to make a career for myself as a programmer. I guess that doesn't say much, though, there's many bad programmers out there who make tons of money.
After university, I did a masters in Data Science. Why I get myself in those situations, I'll never know, but I studied Data Science. By then I knew how to program so I tackled all the maths problems as programming problems. When I didn't get something, I coded it. So I coded a bunch of linear and logistic regressions, and perceptrons and recurrent neural nets and the like, 'till I grokked them right.
So now I'm studying for a PhD in machine learning, in one of the best universities in the world. Not bad for a kid who always got the worse grades at maths, at school, eh?
So what I learned is this:
a) play to your strengths, not your weaknesses.
b) Dont' believe when people say you haven't got what it takes. They only know one way to do things. You do you.
Oh and- you know what? Turns out I'm not that bad at maths after all. Certainly not the discrete maths used in computer science. Not even the continuous maths used in data science and machine learning. I get by.