Out from academy, I was first shocked by the fact that people wouldn't suppose that you know well about things like linear regression, classification, clustering and PCA analyses - you have to prove that you are proficient with those.
Especially when all your past degrees are in biology, rather than those popularly thought as 'numeric' or 'technical' disciplines such as maths, physics or statistics. I could only provide my transcript to show that I got high marks in maths, statistics and physics when I did my undergraduate study and even in mathematical ecology (actually quantitative ecology) from my master's study, if I ever got a chance to be asked.
That's why I am going to pursue a degree in statistics, or rather more directly, in data analytics, to fulfill this gap.
Of course I have yet to learn more skills on myself to be better equipped for career change. I have to use R more proficiently for industrial use, writing more efficient codes and producing more professional illustration. I have to improve my Python skill to be as good as R, and also for its integration into advanced platforms like Hadoop and Spark for large scale data analysis. I have to learn not only SQL but also NoSQL and NewSQL for data ETL.
And also knowledge in operation, supply chain and finance. These are the fields where data analytics is most deployed.
Learning can be endless and I shall pause at some stage and make use of what I have learned, ideally by starting a small project that solves some realistic problem.
Just to make a record what I have been doing for the past two years. Too many thoughts will be a no-no at this time, which have halted me too often. Take action and don't hesitate. Things will turn out when you get close.
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