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.

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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 :).

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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.

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M347 – 33% through and reflections

I’ve recently submitted the first tutor marked assignment of M347, my final course in the BSc Mathematics I’ve been studying with the Open University.  The third unit of this course was long and quite a slog to go through.  While I’ve been using many of these equations over the past few years, diving deep into the theory and derivation has been fascinating, although frustrating due to the lack of practical application.  If you’ve read my other posts then you may recall how frustrated I was with group theory and the early parts of complex analysis, while the quantum world was far more engaging from the start1.  As with all my maths studies, this exercise of filling in the gaps has revealed that there are far more things I didn’t know I didn’t know than things I knew I needed to know.

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Let’s talk about testing

One of the things that I find I have to teach data scientists and ML researchers almost universally is understanding how to test their own code. Too often it’s all about testing the results and not enough about the code. I’ve been saying for a while that a lack of proper testing can trip you up and recently we saw a paper that rippled through academia about a “bug” in some code that everyone used…

A Code Glitch May Have Caused Errors In More Than 100 Published Studies

https://www.vice.com/en_us/article/zmjwda/a-code-glitch-may-have-caused-errors-in-more-than-100-published-studies

The short version of this is that back in 2014, a python protocol was released for calculating molecule structure through NMR shifts1 and many other labs have been using this script over the past 5 years.

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Getting a first job in AI or data science – what candidates need to know

Me in Lego – well not really, but it does look a lot like me 😉 – this was a very fortuitous collector fig from Series 18.

Getting any role in IT can be daunting as a first timer, whether it’s your first ever job or you’ve changed career or you’ve had a break and are returning as a junior in a new field or anything else.  Getting one in any part of AI can be even more of an up hill struggle.  Job posting and recruitment agencies are asking for PhDs, academic papers and post-doctoral research as well as years of experience in industry.  How can you get past that first barrier?  I get a lot of people asking me this when I present at Meet-Ups so thought I’d collate everything into one post.

I’m going to break down how you can demonstrate the skills that businesses need and how to talk confidently abut what you can offer without the fluff.

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Rework London 2019 Part 1

The ReWork Deep Learning summit in London in September has become one of my must have go to conferences. It’s a great mix of academic talks and more practical sessions regarding applications of various types of Ai in business, so I couldn’t miss it this year either. Here’s a summary of Day 1

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OU Mathematics – final module choice

I started this degree not long after my daughter was born, a choice between an MBA and a degree for “fun” (learning for me rather than for career reasons) and it’s strange that I’m so close to the end of it. I have one level 3 module left to take. 30 points (or a quarter of a full time degree) to fit in around work and family1. I wrote a post a couple of years ago on my choices for level 3 and looking back at it now, I’m not sure I feel the same way.

Complex numbers (M337) was far more interesting and practically useful that I thought it was going to be. While it was a little drudgey at the start, towards the end everything came together beautifully. Deterministic and stochastic dynamics (MS327) was dull, it was essential mathematics and really useful, but there was nothing to excite me. Maybe this was a product of trying to study alongside long hours at work at the time. I wish I started with M303, the pure mathematics course, and then done complex numbers and the quantum world (my favourite module so far). However, I now have a choice again and I’m finding it tricky.

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Starting your first AI project – a guide for businesses

I’ve been speaking at several events recently giving practical advice on getting started with AI projects.  There is a huge chasm between high level inspirational business pieces on all the usual sites1 that business leaders read and the “getting started in AI” guides that pretty much start with installing Tensorflow.  There was nothing aimed at the non-AI CTO who didn’t want to fall behind.  Nothing to indicate to them how to start a project, what talent they’d need or even which problems to start with.  Sure, there are a lot of expensive consulting companies out there, but this knowledge shouldn’t be hidden.

This time last year, I sat down with David Kelnar of MMC Ventures and we talked about why so many AI projects don’t succeed.  He asked me to contribute some ideas to be included in the new State of AI report for 2019, to which I gladly agreed.  It soon became clear that to do this justice, it was more than just a chapter, and the MMC AI Playbook was born, which we recently launched.  Contributing to this amazing publication took a lot of time and research, and this blog was the thing that had to give.

If you are trying to find the right time to start your first project and need help on where to begin, please take a look at the playbook.  Here’s a taster, based on talks I gave at Austin Fraser’s #LeadersInTech event and the Barclays AI Frenzy event both in July 2019.

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Quantum Journey – coming to the end of SM358

This week I finished the last of the books in SM358 – the quantum world and am now starting two and a half weeks of intense revision to prepare for the exam. This has been by far the most enjoyable module to study in my Open University mathematics journey so far, even if it was also the first one without face to face tutorials.

While I am very happy at studying on my own, one of the aspects I have really enjoyed in previous modules was spending a few hours every month with fellow students. Not so much to solve problems (as I am always well behind where everyone else is!) but to be inspired. When work and family commitments get overwhelming, study is easy to put to one side, having a checkpoint in the diary helps prioritise and I always left tutorials feeling motivated. I’m not sure whether SM358 didn’t have face to face tutorials because it is a physics module or if it’s just a module that has never been successful with these. Online tutorials are just not the same. Partially because I don’t get home early enough to attend them, but also after a day at work, trying to switch to student mode and find a quiet corner of the house just isn’t possible.

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