You’re not as bad at STEM as you think you are

I was sixteen when I decided I was bad at maths.

I remember it vividly. My year 11 maths teacher Mr. Joyce had asked me to leave the classroom while he handed out the results for the end of year exam in applicable mathematics. After he finished, he came outside and said,

“Well, what do you think you got?”

I think I said something flippant about not caring. And I remember clearly what he said next:

“What do you think you deserve to get?”
Fresh faced and innocent in year 11. Very clearly bad at maths (I am sure Caris and Kelly were better at it than me).

Mr. Joyce and I had had a bad relationship all year. I felt like he picked on me. He was always calling on me in class for the answers to problems whether I’d put my hand up or not and I hated it. Saying “I don’t know” every time he asked a question had become a point of stubborn defiance — a way to deflect attention — whether or not I actually knew the answer.

On some occasions he’d get so exasperated he’d drag my desk away from me — while I was sitting at it — to the front of the classroom and make me sit right under the whiteboard, or take away my text book, or send me outside. He may have been the only teacher ever not to give me a glowing review at parent-teacher evenings.

And now, at the end of year 11 and what had felt like an interminable year of being near the bottom of the smart maths class, he’d brought me outside to gloat over what must have been a terrible result.

I’d failed by half a percent. 49.5%.

I don’t remember feeling much at all about the result, except maybe vindicated — that I’d said all year that I was bad at maths, and this proved it.

And that was that. At the start of year 12, I dropped applicable mathematics and chemistry, switched my attention to humanities and labelled myself a STEM dunce.

I went on to study Law and Arts at university, majoring in English, but strangely enough still found myself working in the tech sector — albeit in policy, project management and strategy roles.

It started with running the Australian Digital Alliance (Canberra), which led into managing digital projects and policy for the International Federation of Libraries (Den Haag, Netherlands). I ended up becoming Head of Policy for the Open Data Institute (London) and an expert adviser in data to one of the UK Secretaries of State (Elisabeth Truss).

And the thing is, while I still labelled myself a maths and science blockhead, I loved working in tech and data. I still do. Tech people are my people.

I like the complexity of technology issues. I like the pace at which the landscape is changing. I like that there are big questions to be figured out in the midst of all of this innovation, about things like ethics and privacy, the impact of automation on people, and what ‘accuracy’ and truth’ mean when it comes to data.

And I love the culture. Openness and collaboration are at the heart of programming, and when you work in an organisation where most of the people have a dev/data science background, it becomes the workplace culture. People share ideas at the early stages of development, and actively seek and encourage feedback. They show their workings for others to learn from. They build things quickly and start testing early. They’re not afraid to talk about failure. They like new ideas, no matter where they come from. Creativity is highly valued. And people are invariably big hearted no-bullshit weirdos.

Even the seemingly normal people in tech have some quiet strangeness, like a deep love of hats or the ability to imitate whale sounds. This isn’t a tech thing. Everyone, in every sector and job, has a weird talent or interest. It’s just in tech these things tend to come out, and — hopefully — be welcomed. I’ve always found I can “be myself” in these jobs. My Beyonce-quoting, board game-loving self.

Maybe I’ve been incredibly lucky. There are lots of douchebags in tech. But in my own experience, they’ve been the exception rather than the rule. The problem is, the douchebaggery shouldn’t be in tech at all and it’s part of what makes women think that STEM isn’t for them.

This brings me back to my own STEM dunce status.

As I said, I am very bad at maths and computer science-y things. I have known this since I was sixteen years old.

But working so much around people who are good at those things, and being married to someone who is good at those things, it’s stopped looking so much like the stuff of magic spells to me. I’ve realised a few things that I wish I’d known when I was wrestling with maths in high school, and which have helped me get into coding and stats now.

I’ve noticed that:

Nobody knows what they’re doing.

Ok, this an exaggeration. What I mean is, there is a lot of trial and error in computer and data science. Really smart people get problems ‘wrong’ all the time in their day to day jobs — they muck up a calculation, they code in a variable incorrectly, they forget to add a square bracket somewhere. Google is your best friend, because Google has introduced you to your new best friend, stack overflow.

Even though I remember receiving marks in high school for showing workings in chemistry and maths, what ultimately mattered was whether you got the problem wrong or right. There wasn’t a culture of learning from your mistakes, or mistakes being part of the process. I took every problem I got wrong as a sign I wasn’t supposed to be doing maths, and was so good at convincing myself of that, that after a while, even the process looked like gobbledygook.

Being good at maths and science is not a special power given only to a few.

In high school, I felt like being ‘good’ and ‘bad’ at things was predetermined. It was inbuilt. Writing, lateral thinking, qualitative analysis came easily to me. I was excelling in subjects like english literature, history and french without having to expend any energy.

Thinking programmatically, on the other hand, did not come easily to me — it wasn’t a natural fit. I simply was not good at maths.

Now I’ve realised that is total crap. I work with lots of extremely smart people in maths and science who treat me as their equal. I keep up. I’m very bright. And I’ve also met a lot of developers and data scientists that I am definitely, 100%, smarter than. There are some dunderheads out there.

There’s no innate reason I shouldn’t be able to do maths and science, I’ve just never been prepared to work at it.

Being able to communicate and apply technical concepts creatively is as important as being able to code.

There are some extremely talented developers and data scientists who struggle to communicate their work in ways that resonate with non-developers. There are people who are great at programming but bad at seeing how something would work in a real world scenario.

I’ve always been a good communicator and a creative thinker. I’m good at explaining complex data concepts; at coming up with new ways of applying them; at seeing all the angles of a problem. These are also valuable skills to have in STEM. I have positive contributions to make.

In my experience, the rarest skill sets (and the most valuable) are those people who are able to do both. And there are lots of people who are capable of being both good at programming and good at explaining programming and seeing how it all fits together. They just tell themselves they’re not good at it.

Women in STEM

I’m conscious I keep using the words ‘people’ and not ‘women’ when I talk being bad at STEM. This is because not all women think they’re bad at STEM, or are bad at STEM. Some women I’m friends with are irritatingly, effortlessly good at maths and coding.

And as much as I’d love to say the only reason I think I’m bad at STEM is because I’m a woman, and that I’m actually an untapped mathematical genius who has been pushed down by society, this is also not true. On the scale of things I try to do, I definitely find maths and programming stuff the hardest.

The message of this blog isn’t so much, “women! You are all secretly great at STEM!”. It’s more, “women! You are not as bad at STEM as you think you are! And you won’t know how good you are until you try.”

Nonetheless, I’m sure that my reluctance in high school to persist with difficult subjects, the sense that I was innately ‘bad’ at maths, that I was meant to do humanities subjects, is partly down to being a girl. I definitely remember that being “too smart” was an actual concern and criticism, and almost entirely directed at smart girls. For the most part, being a smart girl in high school really sucked.

Year 12 cross-dressing day, Melissa and I. Letting our true light shine.

Yesterday the Australian government announced $3.9 million in its first round of funding for new programmes to encourage girls and women to pursue careers in science, technology, engineering and maths. Scrolling through the list of successful programmes, I was struck by how many were targeting girls in high school — providing them with strong STEM female role models, mentorship, a sense of encouragement.

It made me wonder how my own approach to STEM, and learning in general, would have been influenced by the existence of programmes like this when I was there.

I wonder if I’d have been more confident. Maybe I’d think that saying maths is “too hard” was a predictable thing for a girl to say, and want to challenge that. Knowing what a sponge I was in high school, if a ‘cool’ female STEM role model existed I probably would have tried to imitate her in every way, to the point of embarrassment. (Next time you see me, ask me to tell you about the period of my youth when I dressed like a jockey every day).

As it is, I’ve come to STEM in my own way. Maybe some of the revelations I’ve had about not being as bad at maths as I thought I was will be familiar to other women and girls out there. And boys and men too.

As I’ve become aware of the barriers I placed in my own way to being good at STEM, and which started as early as high school, I’ve been removing them. I’ve been teaching myself programming and now spend a lot of time googling error messages. I built my own website — even though I cribbed most of the code from an existing Jekyll template, I’m proud that I could put it all together. It’s trial and error. I’m a smart person. It’s not magic spells.

Next year I’m even starting a masters in data science — all computer science, statistics and applied analytics — and this time, I’m quietly confident I’ll do pretty well.

(Oh god now that I said that I actually have to do well).

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