M347 – Mathematical Statistics – preparing for the exam in the “new normal”

Today I submitted the last assessment ahead of the exam for my tutor to mark in my Mathematical Statistics module. For once, I’m actually on track with my study but it’s not been without difficulty. If you’ve been following my OU journey then you’ll know I work full time and have a family, so dedicated study time can often be a low priority. Up until the second week of March this year1 I had a reasonable routine: I’d spend the two hours I commute Monday to Friday going through the course materials and then this extra maths wouldn’t impact work or home life.

Once we were all working from home I didn’t have that time any more. I was still working 40+ hours a week and, like many others, was suddenly coping with having to educate my daughter for more than twice the time I’d normally spend commuting. Jealously I was seeing posts on social media from my friends “making memories” with their families, while I was working from 6am until 8.30am and then breakfast and school until 1pm then a quick lunch and back to work until after 7pm. By the time I’d had dinner and done some chores, it was approaching midnight and the routine would start again. As a result, for the first four weeks of lockdown, I didn’t do any maths, or really anything else above work and school. I was exhausted. Even with the two days off work due to the Bank Holidays at Easter, motivation and energy were pretty non existent for anything that wasn’t critical. I know I’m not alone in that. It’s taken a while for everyone to adjust to this new normal

6 days worth of sleep and active analysis showing 5-6 hours sleep per day
Good job 6 hours sleep is enough to function 😉 – this is what a working week looks like for me right now.

We got informed the exam would still be happening at the start of June, but it would be released for us to do at home. I had been secretly hoping they would just score us on our coursework so far, but while that would have worked for me, it would not have been fair. In M347, the continuous assessment does not constitute part of your final grade – you only have to submit a proportion of the assignments (13 out of 18) and you only need 30% overall. There are many students who would have skipped some of these and also not put in full effort because they didn’t have to – particularly when “life” gets in the way2.

Once I knew there was an exam still to come, I realised I had to catch up. Two weeks ago I was 7 weeks of study behind. That was going to take a lot of catch up. There was no time in the week, so I have had to give up my weekends. With the exception of the one Saturday when I played World of Warcraft to vent the anger at my neighbours deciding that 8am was an appropriate time to start drilling on our adjoining wall3, Saturdays and Sundays have been catchup time. After breakfast, I’ve shut myself away and ploughed through the study books and the outstanding computer marked assignments, surfacing one an hour for a cup of tea and to stretch my legs. Last night it was midnight by the time I finished, but I was too stubborn to stop until I’d got to the end of the chapter so I could do the final assignment today.

It’s not been easy but I’ve had help. My daughter is old enough now that she can keep herself occupied and both she and my husband have taken their share of the housework. If we weren’t in lockdown I wouldn’t have left the house any way to catch up, but I do feel I’ve missed out on some of the fun, family time, particularly at the weekends. But this is, like the lockdown itself, short term sacrifice for a bigger goal.

Progress bar for M347 showing 92% completed.
One chapter and exam revision to go!

This afternoon, I submitted the last assignment and it feels good. I have one more chapter to go and the revision, but I’m on track for the first time since this course started and for the first time at this point of the year for any of the modules! I can only hope the exam goes as well.

If you’re finding yourself struggling while studying remotely, reach out to your fellow students on forums, on social media. There are virtual study groups being organised. Your tutors are still there to help you. Keep focussed on why you started these courses. You’ve got this.

Remote Data Science – Interview

Last week I was interviewed by Keith Robinson of Ammonite Data, with a topic of managing data science teams remotely and all the challenges this brings. We had a much more wide ranging conversation where I looked at challenges of communication and even the impact on models that the current extraordinary events will have.

Part 1: Where I discuss communication and mental health while isolated
Part 2: Where I discuss the current data blip, security and consent, and prioritising work in crisis mode.

I hope you find these enjoyable and helpful.

Review: Lego Ideas International Space Station

For the past two weeks I have, like most people, been working from home. Doing fun stuff just for yourself during this time can be incredibly important, and with this in mind I’ve started going through some of the Lego and other building kits I’ve got and just not had time to open. The first of these that I’ve tackled is this years Ideas (fan-designed) Lego set the international space station.

The unopened box.
Continue reading Review: Lego Ideas International Space Station

Data Science Courses – the missing skills you need

One of the things that I have been complaining about with many of the data science masters courses is that they are missing a lot of the basic skills that are essential for you to be able to be effective in a business situation. It’s one of the things I was going to talk about at the Women in AI event that was postponed this week and I’m more than happy to work with universities who want to help build a course1. That said, some universities are realising this is missing and adding it as optional courses.

Continue reading Data Science Courses – the missing skills you need

A diagnostic tale of docker

twenty sided die showing common excuses for developers not to fix problems, the top of the die shows "Can't reproduce"
Developer d20 gives the answer 🙂 (from Pretend Store)

If you’ve been to any of my technical talks over the past year or so then you’ll know I’m a huge advocate for running AI models as api services within docker containers and using services like cloud formation to give scalability. One of the issues with this is that when you get problems in production they can be difficult to trace. Methodical diagnostics of code rather than data is a skill that is not that common in the AI community and something that comes with experience. Here’s a breakdown of one of these types of problems, the diagnostics to find the cause and the eventual fix, all of which you’re going to need to know if you want to use these types of services.

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Mathematics of player levels in game development

My husband is a game developer and my contributions are usually of the sort where I look at what he’s done and say “hey wouldn’t it be great if it did this”. While these are usually positive ideas, they’re mostly a pain to code in. Today however, I was able to contribute some of my maths knowledge to help balance out one of his games.

Using an open api, he’d written a simple pokemon battle game to be used on twitch by one of our favourite streamers, FederalGhosts, and needed a way of determining player level based on the number of wins, and the number of wins required to reach the next level without recursion. While this post is specifically about the win to level relationship, you could use any progression statistic by applying scaling. Here we want to determine:

  • Number of wins (w) required for a given level (l)
  • The current player level (pl) given a number of wins (pw)
  • Wins remaining to the next level (wr) for a player based on current wins (pw)

Let’s take a look at a few ways of doing this. Each section below has the equations and code examples in python1. Assume all code samples have the following at the top:

import math

database = [
{"name": "player1", "wins": 5},
{"name": "player2", "wins": 15},
{"name": "player3", "wins": 25}
]
Continue reading Mathematics of player levels in game development

Data: access and ethics

Last week I attended two events back to back discussing all things data, but from different angles. The first, Open Data, hosted by the Economist was an event looking at how businesses want to use data and the ethical (legal) means that they can acquire it. The second was a round table discussion of practitioners that I chaired hosted by Ammonite Data, where we mainly focussed on the need for compliance and balancing protection of personal data with the access that our companies need in order to do business effectively.

We’re in a world driven by data. If you don’t have data then you can’t compete. While individuals are getting more protective over their data and understanding its value, businesses are increasingly wanting access to more and more – at what point does legitimate interest or consumer need cross the line?

Continue reading Data: access and ethics

How much maths do you really need for data science?

My LinkedIn news feed was lit up last week by a medium post from Dario Radečić originally posted in December 2019 discussing how much maths is really needed for a job in data science. He starts with berating the answers from the Quora posts by the PhD braniacs who demand you know everything… While the article is fairly light hearted and is probably more an encouragement piece to anyone currently studying or trying to get that first job in data science, I felt that, as someone who hires data scientists1, I could add some substance from the other side.

Continue reading How much maths do you really need for data science?

To everything there is a season

Depiction of Janus, Vatican collection, Photograph by Loudon dodd – Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=7404342

It’s inevitable, at the start of a new year, to reflect on what has gone before and what is yet to come. Janus, the Roman God for whom January is named 1, is depicted in such a way, so it’s difficult at this time of year to be anything other than retrospective :).

Continue reading To everything there is a season

Facebook’s Maths Solving AI

In December, Lample and Charton from Facebook’s Artificial Intelligence Research group published a paper stating that they had created an AI application that outperformed systems such as Matlab and Mathematica when presented with complex equations. Is this a huge leap forward or just an obvious extension of maths solving systems that have been around for years? Let’s take a look.

Continue reading Facebook’s Maths Solving AI