How much math do you use in your PM role, and what kind of math?

Although I am not a technical or data scientist PM, I get the impression that in order to excel in the position, I need to brush up on my math abilities.

I’d prefer to self-train and prepare for processing huge user datasets, but I’m curious if there’s anything else on this topic that others may comment on.

I studied statistics in college and did calculus back in high school, but I don’t recall much of it.

Any recommendations?


In my experience, after basic math up to algebra, statistics would be most useful.

But I really don’t use that much.


I’m only doing math for financial purposes. If ever something sophisticated is required, there is generally someone else whose duty it is to perform certain kinds of math.

In the three PM positions I’ve held, statistics—like the kind used to examine user sentiment and behavior—have always felt like a missing component. However, management never thought it was wise to have that level of consumer understanding in any of my responsibilities. It was nearly always what I refer to as “product management by ego” in this sense.


Appreciate that @MariaWilson! I really understand the sentiment, but the good news is that I think our culture can soothe egos with solid information. It simply requires doing it.


Math is not that significant. Not in my experience, at least. A PM mostly serves as a liaison between teams. Always the generalist; never the specialist. Maybe a more crucial question to ask oneself is how well you speak the language.



It cannot be emphasised enough that a successful PM brings professionals together at the appropriate moment to achieve the desired results.

Many users here want to learn more about programming, or other topics where you might work with an expert partner to achieve business goals.


Regression statistics predominate. In an afternoon, you can learn everything you need to get by. Algebra throughout high school, except than that. You should be all set if you learn SQL and Excel.


@PriyaVarma, out of curiosity, have you ever read Data Smart?

I am more proficient in SQL than Excel, but how in-depth would you expect? What else do I need to know except the fundamentals—VLookup, Text to Column, and a little bit of Match Index? Any particular area of analysis that you would suggest?


@PouyaTaaghol, I’ve read Data Smart, it’s ok and probably more advanced than you’ll need unless your shop is ridiculously lean.

You’re ahead of the game if you know how to handle strings, write nested if statements, count, sum, and sum products.

Nowadays, I really prefer using sheets over older versions of Excel because it’s simpler to collaborate with others and some of the “magic” capabilities operate a lot faster.


Interesting! Which industry/“scene” are you in?


HR Tech. I was a bi developer before this (used a lot of Python, SQL, and SaaS), and I then worked as a consultant for a while. Having said that, I had to show my previous boss how to write vlookups. He helped launch YouTube’s ad suite, which in no way lessens his product or marketing expertise. Simple Excel and math abilities make life simpler. Anyone can learn this; your best friends are YouTube and StackOverflow.


Why do you think you need to improve your math abilities?

Are there particular requests that you get into trouble answering?

For what kinds of goods do you work as a PM?

Which types of datasets are you able to analyse?

I mostly use accounting-related math for business cases on a daily basis, such as GTM revenue ramps, cost analyses, ROI calculations, and pricing calcs.

In the past, I examined sizable datasets that documented user behaviour to learn more about their routines and usage patterns. Basic statistics like mean, median, mode, and standard deviation were useful in this, as well as research statistics like ANOVA, f-tests, and z-tests.

Erlang equations were helpful for capacity planning in an application I worked on for contact centres.

In the era of AI and ML, it may also be helpful to comprehend more intricate ideas.

According to my assessment, it differs depending on your sector, product, and PM role focus.

I might be able to offer more specific suggestions if you give me some responses to the aforementioned questions.

  1. We have an AI component to our company that I don’t directly handle, but I believe I may need to brush up on or learn Calc for managing interacting with Data Scientists - no one has expressly approached me about this, but

  2. No, that terrifies me the most. I’m concerned that we aren’t conducting enough (or the appropriate) analysis, and I want to ensure that I can present arguments that are better supported by facts.

  3. SaaS/B2B, young market, some customers, but not yet a perfect fit in terms of product and market. It somewhat resembles that we are painting the walls.

Specifically, I want to expand my skill set beyond what my current work requires of me in order to transition into other companies, such as video games, consumer goods, or “consumery” SaaS enterprises.

I’m also interested in learning more about the Stats and Erlang use-cases. That might be an excellent direction to go in.

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I use math daily but I have an advanced degree with an emphasis on statistics and econometrics. It’s definitely an advantage in some situations, especially when testing.