Everyday Examples of Data Use

Data analytics is already all around you — even if you don’t see it.

• Streaming apps recommend what to watch
• Fitness apps track your progress
• Navigation tools suggest faster routes
• Online stores offer personalized suggestions
• Banks monitor transactions for fraud

These actions are powered by simple data pipelines and calculations, just like the ones you’ll build in this course.

Imagine your phone or watch tracks how many steps you take each day.
It wants to show your weekly average at the end of the week.

This can be calculated with a few lines of Python:

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# Step counts for each day of the week
daily_steps = [8120, 9200, 7890, 10230, 8675, 9340, 10000]

# Average = total steps divided by number of days
average = sum(daily_steps) / len(daily_steps)

print("Average daily steps:", int(average))

Run the code block on the right side of the screen to see the result. You’ll get the average number of daily steps based on the weekly data.

This is a simple example — but the idea is powerful. With larger datasets, you'll soon use NumPy and Pandas to process thousands or millions of rows just like this.

The core concepts of analytics — aggregate, compare, and interpret — are used in every industry.

And as you’ll see throughout this course, those skills start with basic tools like the one above.