We come across information everywhere on the internet, at school, on social media. Numbers, charts, news, opinions… But which ones are true? Which ones actually help us understand something?
To answer these questions, we need a skill: information and data literacy.
Information and data literacy means knowing where to find information, checking if the source is trustworthy, understanding data (like numbers and graphs), and explaining it clearly to others. It’s not just about finding information. It’s about understanding it, questioning it, and using it wisely.
As Francis Bacon said, “Knowledge is power.” But that power only comes from accurate knowledge. If we believe something that’s wrong, we might make poor decisions.
For students, this skill is especially important. It helps us:
Tell the difference between reliable and unreliable sources
Use correct data in school projects
Think critically about what we see online
Share information in a clear and meaningful way
For example, if you’re doing a science project about how temperature affects plant growth, you need to collect and read your data carefully. If you misread the numbers, your conclusion might be wrong.
Dan Heath once said, “Data are just summaries of thousands of stories. Tell a few of those stories to help make the data meaningful.” So data isn’t just about numbers. It’s about understanding and sharing what those numbers really mean.
Being data literate helps you make smarter choices, stand out in school and future jobs, use technology better, and contribute to your community. It’s not just about being a good student. It’s about being a thoughtful problem-solver.
Today, it’s easy to find information. But understanding it, questioning it, and using it well. That’s what really matters. Information and data literacy is a superpower for the future. And it’s a skill you can start building right now.
I built and programmed the circuit at home and introduced curious children to the magical world of electronics.
I built the circuit. I assembled the robot (which was a bit difficult with basketball player hands). I programmed it and introduced this robot to curious children.
How I used IoT to help detect wildfires early
During this internship, I developed and designed an independent network of small temperature sensors, or beacons, to catch wildfires very early. This system uses LPWAN (Low-Power Wide-Area Network). The main goal was simple: to find small hot spots quickly, well before the fire starts or anyone sees smoke. This way, fire crews can arrive much faster and reduce damage.
Scope of Internship work:
1. System Blueprint: I planned how all the different components would work together, including the sensor nodes, the network hubs (gateways), and the main cloud server that collects all the data. It felt like designing a big team of tiny spies!
2. Smart Anomaly Detection: I wrote the code (the algorithm) to analyze the temperature data from high-precision digital thermometers. This code learns what "normal" looks like and quickly flags anything unusual. I made sure it checks with nearby sensors to confirm that it wasn’t a false alarm.
3. Battery Life Hacking (Energy Efficiency): Since these sensors need to remain in the forest for months, I looked into the best ways for them to communicate without running out of battery.
4. Instant Location Alert: I ensured that if the system detects an anomaly, it sends the exact GPS coordinates right away. This gives fire management teams a specific, actionable location rather than a general warning.
This whole project was great because I got to apply my textbook knowledge of IoT in real life.
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