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Data science is actually quite simple. Simple, but not easy.
I will try to keep this blog post somewhat short and sweet. Cut thru all those current fashionable bandwagon hype, trivial klik-klak chitchat clutter, and technical smarty-pants mumbo-jumbo jargons. There are plenty of rocket science experts providing in-depth technical definitions about data science online – so I’ll just keep it real K.I.S.S here.
As with any good copywriter (note difference from ‘copyrighter’) will say, content post length is like a miniskirt on a gorgeous brunette (see, I’m about diversity, don’t discriminate, and maintain a neutral average stance across black to blonde haired beauty spectrum) – a hot miniskirt that is short enough to entice but long enough to cover the essentials. Keeping your attention from the beginning to the very end. Go stickiness and engagement.
With that promise in mind, here we go.
In it’s pure core age-old fundamental essence, DATA SCIENCE is both the art and science of transforming data into useful information and actionable insights, via whatever tools and means available – thus ultimately adding value and impactful knowledge to individuals, enterprises, societies worldwide (within our Earthian framework AFAIK). Validating assumptions and guiding better decisions. Yippee ki-yay MF. Winning.
That simple? Yes, that’s all there really is to it. No mess, no fuss, definition done and dusted. Oh, did I just rain on this sophisticated bigger-than-benhur parade. =)
Alright, look. Despite popular belief on ‘normal’ popularly accepted convention, Data science is often misunderstood. It is not just about Python or R programming platforms. Nor just AI Machine Learning Deep Learning algorithms. Nor just advanced statistics or predictive modeling. Nor just SQL or other query languages. Nor just big data or data clouds. Or any whatever other fancy schmancy tools or specific woohoo data specialisation fields. This is a common misnomer that most recruiters, hiring managers, and wannabes wrongly pigeon-hole and misuse role titles and descriptions. Don’t get me wrong, it’s not their fault for being misinformed. Anyone trying to push their own definitions or agendas are more than free to do so. More power to paying bills. However, we know better on the long-term holistic truth. Rise above, you cream of the crops.
Collaborative Knowledge Sharing vs Tall-Poppy Syndrome Crab Mentality. Abundance vs Poverty mindset. Rise-Above Empowering Scepticism vs Desperate Petty snarky Cynicism. Blue ocean vs Red ocean strategy. Global vs Local awareness. Being former or the latter – your choice. Get woked man.
Data science has always been around. Same stuff but different styles. Same ol’ story but different angle. Same commodity substance but different packaging. Same generic crappy shit but different clappy branding. Etc et cetera and so on and so forth. Whilst good storytelling is essential to effective data science, bad storytelling or BS excuses just don’t cut it. A polished turn – is still a turd. Do not be fooled nor impressed by technical complexities, nor corporate posers or data snake-oil salespersons. Do not confuse real history/culture from politics, real science/philosophy from religion, real awareness/justice from social/psychological peer pressure/compliance. Digressing… oops.
Skillsets, process, and resources that enable effective/efficient data science is again quite simple, but not easy. Various tools and platforms are a part of this exciting journey. But the secret is that that is no secret other than 1% ‘one-time’ inspiration (A.K.A. sweet-talks) followed by 99% ‘real-deal’ perspiration (A.K.A. implementation).
Data science increases transparency, visibility, and accountability across all areas of commercial, professional, and personal arenas. Without a doubt. The ethos and scientific practice of keeping company agendas and deliverables very real. And ultimately optimising and delivering that competitive edge to winning the economic game.
Let’s be real honest here now. The truth of the matter is this. Most organisations need to get the basic logic and process fundamentals sorted out first, before jumping onto the next latest greatest and shiny new sexy tools and platforms. Thing big think strategic think long term.
Data scientists that typically get employed, are hired more on technical tools/platform experience requirements, but less so on real-world common-sense and commercial/strategy applications. Again, short-sighted tactics and short-term KPI’s – as opposed to big vision strategy and long term wins. A real common mistake. Hit that monthly hiring quota, baby.
Just like how marketing is commonly misused and limited to just being basic advertising and creative content creation (by most so-called corporate marketing ‘professionals’). Whereas it covers a wider scope of branding/USP, sales/pricing strategy, product development, market research, competitor intelligence, customer/consumer/client engagement/relationship and lifecyle/journey management, conversion optimisation, personalisation, and much more. Keep the marketing-mix (4 P’s, 5 P’s, 6 P’s, 7 P’s… n P’s) lingo speak to theoretical textbooks – wow so smart. I refer to real-world learning and earning experiences in the real-life trenches. But digressing again on another topic – so moving on and getting back on track.
Data science is only started gaining traction and respect it deserves over the last few years. It’s more than just a passing fad (unlike such one hit wonders, sexy dances of internet marketing, social media, mobility, blockchain, etc – of which data science is the essence that will drive all that). You can bet your bottom cryptocurrency that we’re just scratching the surface on data science, and this shit is about to get real – real fast.
That’s right. Data science is about keeping it real, and cutting thru all those posturing drama and posing fashion parades, with an unabashed No-BS attitude. Keeping things in check, being more holistic with more long term strategic approach, and being more understanding about the what, why, and how the world works.
Mark my words. The once underrated mistreated and disrespected analysts and data geeks of the world are now turning the tables and dominating the relevant discussions.
Exciting times ahead. Stay tuned.