Page 28 - Touhpad Ai

u Create a Google Form or printed questionnaire with the following fields:
Screen Time (hrs/day) Social Media (hrs/day) Phone Unlocks Bedtime Productivity (1-5)
5 2.5 60 11:00 PM 3
7 4 100 12:30 AM 2
3 1 40 10:00 PM 4
Aim to collect responses from at least 20-30 students. Make sure to inform them that it’s a learning exercise and that
data will be used respectfully.

Step 3 Exploring the Data
After collecting the data, use Excel, Google Sheets, or Python to explore it. Key steps include:
u Check for missing or incorrect values (e.g., impossible screen time or bedtime entries).
u Calculate averages, medians, and standard deviations for each variable.
u Use scatter plots, bar charts, or box plots to identify patterns and trends.
u Examine relationships between variables, such as screen time vs. productivity or bedtime.
u Identify unusual data points (e.g., 12 hours of daily screen time) and handle appropriately.
You might notice, for example, that students with over 6 hours of screen time generally rate their productivity lower.
Or, you might find no clear pattern, which is also an important insight! Note the following in your notebook:
1. What patterns or trends did you discover?
2. Does more phone usage lead to lower productivity or later sleep?
3. What surprised you in the data?

As you continue your journey into AI, always remember: “Good data leads to good decisions.” So, spend time
understanding your data—because that is where the magic begins!

During Hurricane Frances (2004), Walmart leveraged automated data analytics
to predict customer buying patterns before the disaster. Instead of manually analysing
sales data from thousands of stores, Walmart used advanced data processing systems to
clean and organise historical transaction data, weather reports, and disaster-related buying
BRAINY trends. The analysis revealed that strawberry Pop-Tarts and beer were among the most
FACT in-demand items before hurricanes. Walmart strategically pre-stocked these products,

ensuring better service and minimising shortages. This case highlights how automated data
preparation enables businesses to efficiently process large datasets, make quick decisions,
and improve customer service during emergencies.

Evolution of Computing
The evolution of computing traces the journey from simple calculation tools to the advanced, interconnected digital
systems that drive today's world. This transformation spans several generations, each marked by major technological
breakthroughs. The journey began with mechanical computing devices like the abacus and Charles Babbage’s Analytical
Engine, which laid the foundation for modern computers.

First Generation (1940–1956): Vacuum Tubes

Computers in this era used vacuum tubes for circuitry and memory, which made them massive, energy-intensive, and
prone to overheating. Primarily designed for scientific and military applications, these machines were mostly confined to
research laboratories. Example includes ENIAC and UNIVAC.

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