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Speaker Series Session 3: MATH

Author: Fariha Khan, Co-founder (blog content was extracted from speaker's presentation)


Speaker Series Session: 3

STEM Topic: Mathematics (Statistics)

Speaker(s): Joy Wu



Joy Wu was the feature professional for our third session of our Speaker Series under the category of Math. Joy graduated from the University of Toronto with an HBSc specializing in Statistics and minoring in Mathematics. She currently works as a Data Associate at WatrHub, a data intelligence company focused on improving the public in North America. She worked through internships at different financial institutions and was a guest speaker on behalf of her department’s Internship and Mentorship program.


Wu’s presentation was very informative, offering a macro and micro perspective on the topic of statistics and how she has ended up where she is today. She offered a lot of insight from her journey throughout the presentation and we’ll try to weave in the advice into the blog in the same order.


Wu took an unconventional path, one which included switching majors, interning at odd times throughout the year, and finishing her degree and program requirements during her time on an Exchange trip. For her, it was always about finding a balance between seizing opportunities present at any given moment, while still leaving room to plan ahead. As a result, she recommends students to go on exchange if they have the opportunity. Wu knew this was something she valued and thus made sure she did everything possible to maximize her probability of graduating.


Now, let’s dive into how she defined statistic. Wu gave us a simplified understanding of statistical work divided into four steps:


1. Collect

  • Collect a bunch of data from a variety of different sources

  • Surveys

  • Private sources (Google user information)

  • Clinical trials

  • And sometimes data primary data needs to be created using computer programs executing different mathematical techniques

  • Note: this really depends on the field!

2. Clean

  • The real world is messy

  • Items are missing, incorrect, and sometimes make no sense

  • There are guidelines to follow when cleaning so that the integrity of the data is not disrupted. Changing the data too much when cleaning can misrepresent the the real world situation

3. Analyze

  • Two phases: 1) Exploratory 2) Explanatory

  • When you explore, you’re looking at different trends in the data, as well as things that stand out

  • In the explanatory phase, you’re summarizing the findings in the exploratory phase, and using it to tell a story

  • Most of the time, there will be a specific question you would want to be answered to help offer guidance through the phase

4. Present

  • Self explanatory here - the results are presented



Wu wanted to highlight something very important - statistics tell us about the world, but they are not perfect! She quoted George E.P. Box “Essentially, all models are wrong, but some are useful.” Statistics are our best approximation and are very useful in many, many fields. However one should always think critically about the information they come across.


There are three key ways to help you get into the field. The first is by gaining the fundamentals through school. Some schools she mentioned in Ontario included the University of Toronto, Waterloo, Queens, and Western. The second is by exploring what others do through what’s available online (examples on our resources page). The third is doing it your own way by playing with data through challenges and surfing open data sources (again, more examples on our resources page).


We end all of our sessions with a Q&A. Here are some of the questions we asked:


What is the best advice you’ve ever received?

Keep things simple. Only you know what’s best for you. Block out the noise whether it’s direct or indirect. People may be posting achievements, but do not let that demotivate you.


How do you balance your personal and professional life?

Physical separation - don’t sleep where you work. It helps set the boundaries for the mental separation as well. I enjoy my work, but found it difficult to distance once I completed it.


What was the transition into stats like?

As long as you are able to take the classes and pass them, you can switch. In first year, I took more general courses. Any courses I missed to major in stats, I took in summer school to catch up. Overall, it was pretty seamless. I was also fortunate to be in a position where extending my program an extra year wasn’t a financial burden. In the end, do what is right for you and be informed as much as possible. Talk to your professors, not just admin.


Have you ever experienced direct gender bias in your field and how did you overcome it?

I haven’t experienced it, but I have seen it by looking up at the corporate ladder. It’s exhausting to know that going forward everything you do has to be justified. It’s good to keep a mental reminder of “am I feeling this way because I’m being pushy or because I’m coming across as a bossy woman?” A song that reminds me of this feeling is Taylor Swift’s “The Man”.


Although there are no men currently attending this session, how can they play a more active role in helping reduce these gender gaps?

Put yourself in other people’s shoes whether that be mansplaining or anything else. Think about what you’re saying or doing from the perspective of someone else.

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