My own re-skilling journey

Just over a year ago I embarked on an educational journey.

COVID-19 pushed many people to acquire new skills and I was no exception. I considered taking short courses, doing a business Master’s degree, and even filed a PhD proposal (which was accepted, but I didn’t proceed).

I knew I wanted to broaden my understanding of modern technologies – food, finance, telecommunications, and particularly data science and artificial intelligence. While I had been working with data analytics for a while, my skills were pretty one dimensional and my understanding of contemporary analytical tools – pretty shallow. I wanted to once and for all have an answer on whether the robot uprising was plausible and (just in case it was) what I needed to do to defend myself, my family and humanity.

After weighing long lists of pros and cons, I chose to do a Master of Science in Computer and Mathematical Sciences at AUT. It offered a good range of deep technical subjects, as well as a lot of freedom on what subjects I could choose. As much as I am a violent supporter of online learning, in this case it wasn’t for me. The knowledge gap was too wide and the motivation was still strengthening – I wanted to learn from real people. I am 2/3 through now and the experience sure didn’t disappoint!

I learned to code in Python, R, SAS and SQL and use an array of other tools. I seriously brushed up on my calculus and linear algebra and expanded my knowledge of statistical models.

Have I become a Data Scientist or a Software Developer? Or will I?

I haven’t. And I don’t know – maybe.

I now know I could become a decent developer if I put time into it, but pure software development is probably not my gig. I love the data science algorithms and methods I learned – particularly in multivariate analysis and machine learning. I think back to a few challenging problems in my remuneration management and people analytics career and think – it would have been nice to know these then! However, if I am honest, the really juicy business problems that data science can help resolve – are still few and far in between. Businesses simply don’t have enough quality structured data. Hence currently, the more rewarding job is actually getting organisations to collect this structured data - through behavioural change and process improvement, rather than trying to analyse what is available. Here we go again.. seems like all roads lead to behavioural change.

If I decide to continue my technical education in data science, it will be into Big Data tools. They look promising in handling the unstructured data. However even if I don’t end up changing career – this journey wasn’t in vain! I can now confidently state that a robot uprising is unlikely. However, a re-distribution of wealth and power in the human world is almost inevitable. And those who have the information will hold the power (Google and AWS I am looking at you).

What is (literally) burning on my mind these days (and the minds of many others) is how much we need to change to avoid a climate disaster. My rekindled ability to consume the irrefutable numerical evidence has made it really challenging for me to ignore it, and here’s the twist… just like the data analytics issue, fixing the looming climate catastrophe will also come down to (seemingly simple) changes in human behaviour.