#opinion

There is a current trend, where company values are “creativity”, “innovation”. But how do we encourage these things? Learning to Fail Creativity : the use of imagination or original ideas to create something; inventiveness. – Google (Side note, using the word “create” as a definition of “creativity” is probably poor form…since if you didn’t know the definition of creativity, you probably don’t know the definition of create!) You can’t be creative, without creating. [Read More]

I am a minimalist. Perhaps not an extreme one, but am always looking for ways to do less. This isn’t always easy, infact it is increasingly hard. Often times you find yourself more and more engrossed in elements such as the daily news for example, or the latest trends. I had come to a moment of serendipity (is this correct usage?). When I aimed to replace my current smartphone (HTC Trophy) with Firefox OS phone from ZTE Open. [Read More]

This is a statement which I’ve been thinking about extensively for a while now. How do you become a data scientist? (or rather how should I become a data scientist). The conclusion that I have now (subject to change) is not MOOCs. Without a doubt, MOOCs will help, infact I’m eyeing an article right now and following it as the basis of improving my knowledge. To be fair the article also links an additional two articles on management and [technical] (http://www. [Read More]

After working full time for one year, one of the stumbling blocks for (good) analysts producing results is tools. There is such a huge emphasis on what tools you should be using, rather than how you should use it! Big Data is all the rage and interest right now, and perhaps that might be what is to blame. ##A Shifting Focus? The industry may be moving in the right direction. With the introduction of MOOCs like Udacity, Coursera, and EdX, vigilant and proactive computer programmers or statisticians can increasingly improve and widen their knowledge. [Read More]

Being technically right. Theres many variations to this theme, including code smell. But what about data analytics? We know that we can obviously have code smell within our own programming, whether it be in SAS, R or any other programming language we intend on using. But what about when dealing with business users? Is there some equivalent to code smell? Consider this situation: A business user requests for information You promptly and quickly provide precisely what was requested; in a spreadsheet with 5 tabs, each tab containing 1000 rows. [Read More]

The biggest lesson I’ve learnt in my short time in analytics is being vigilant on sticking with a single source of truth. Every extract should be run once, cleansed through once, and then thrown into production. There should not be multiple extracts for the same ‘theme’ as this serves only to confuse not only other people but yourself (at least in my experience). Simplicity should be strived for; having one go to source will solve that issue. [Read More]

Too often, prototype creep occurs. Prototypes end up being the production version, regardless of whether it is appropriate. “We don’t have enough time.” Is the most common excuses for not creating a production copy. However, we really should plan accordingly. There are many reasons for creating production versions from scratch. One being: Once in production, a prototype will never die. This is why “we don’t have enough time. [Read More]

A great sin in many data analysts (especially statisticians) is repeating themselves. We need to learn from programmers over the last few decades how to code efficiently and effectively. Perhaps the easiest and quickest starting point is documentation. Why do we keep code description in our header comments and maintain separate documentation in a word document? One easy solution would be to write a program which extracts the comments from all your header files and generates the required documentation automatically through a markup language. [Read More]