Data Science (for Kids?)

Here is a personal story behind the ideation and implementation of the Data Science for Kids program that some of you may be interested in:

I have one belief: all children are born innately curious. And it’s our responsibility to do the best we can to encourage and motivate them to continue to be an explorer since young.

Why Data Science for Kids?

Personally, my inquisitive mind has always made me curious about so many things around me, from the elementary question of “why is the sky blue” at 5 to “what if I reverse the equation” during Physics class to “what could I have done differently under that situation” when dealing with friendships. It all made sense to me one day, that I am who I am today because of this trait of mine that I carried through even when I was being commented about my questions especially during school times. My curiosity made me:

1. understand more about myself, people, relationships and the world around me;
2. learn more actively and avidly;
3. flexible and adaptable in ever changing situations; and
4. last but not least, a better Data Scientist today.

On point #4: I realized that one of the first requirements to be a passionate Data Scientist is if one is able to ask questions without much effort; actually, ask the questions that guide you in the right direction; to be precise, ask the right questions to ultimately present best probable solution for the business. Every stage in the entire Data Science Lifecycle especially business understanding/problem statement framing, exploratory data analysis, feature engineering and model evaluation requires strategic thinking, questioning and solutioning.

“What’s business’s pain point(s)? Does their current process really need improvements? Are they ready to adopt digital solution or new ways of working?”

“Why does data not support business hypothesis? Are there other variables that we or business may not have considered during the analysis?”

“Can the model performance really be that good albeit the poor data quality? Will business require model explanations? How should we simplify the interpretability?”

Without strategically thinking deep and through, you’re as good as a computer executing tasks.

That said, there’s a catch. You bet, we can ask never-ending questions, form endless hypotheses after investigating one, essentially “dig deeper/rabbit holes”. Some call it analysis paralysis. It is another topic to be addressed separately in another article. But essentially, the trick lies in:

1.??????mastering the framework to ask questions that matter and enable you to come up with a feasible solution within stipulated time; and

2.??????prioritizing decision-focused investigations so that actionable insights can be derived from it.

I’ve highlighted the importance of asking questions and how I connected my inquisitive self to being a Data Scientist. When not solving business problems, am I still a Data Scientist? Have I been a Data Scientist all these while because here’s a sudden realization: I’ve been dealing with data and models my entire life – what I ate yesterday is data; guessing how my dad would react after my mom said certain things is a prediction; downloading knowledge to my brother about what to pay attention to so that my mom isn’t triggered by what’s about to come out of his mouth is transfer learning; My answer is 66.6% YES. Because the other 1/3 is putting these into practical and scalable frameworks through coding which I didn’t acquire until my college years.

How is Data Science for Kids conducted?

Tell me and I forget. Teach me and I remember. Involve me and I learn. - Benjamin Franklin

This program is carefully crafted to make children realize that they already have the superpower in them and that we’re all Data Scientists/Explorers because we’re born to wonder about things, gather information, recognize patterns, form relationships and make guesses about the uncertainties and the future. This program gives them the right encouragement and framework to pen their thoughts and processes down. It guides and brings them through the entire Data Science Lifecycle: (1) curiosity and wonders exploration; (2) problem statements and hypothesis formulation; (3) data collection; (4) data analysis and treatment through performing statistics; (5) data visualization; (6) model building and evaluation; and (7) presentation. The children are guided through active involvement and hands-on activities so that they learn. For example, in introducing clustering under unsupervised learning, they’re given group activities where they need to create N clusters based on features they collectively come to agreement on. The hypothesis is that involving them in thinking-out-loud brainstorm discussions would make unsupervised learning concepts more intuitive and digestible. All in all, this program serves as an ignition – so they incorporate Data Science concepts in their everyday lives and continue to ask questions, the right questions.

I’m thankful for this experience. I hereby express my sincerest gratitude and appreciation to Girls in Tech Kuala Lumpur, Microsoft, all volunteers, facilitators, contributors and sponsors (My Virtual Foodhall) because without them, the program wouldn’t have been this successful. Thanks to all parents for providing your children and us this opportunity to expose our children to Data Science concepts.

Postface:

With current availability of AI and technology, we can search for answers and communicate instantaneously. Gone are the days we were tested and evaluated mostly based on the precision of the definitions of words and terms. Rather, the world tests our creativity, problem-solving skills, agility and ability to articulate our ideas and thoughts. New explorations and experiences especially hands-on and outdoor ones enable these.

p/s: none of these are generated by ChatGPT.

Thank you for reading. Looking forward to receiving your feedbacks and thoughts!