Everyday Examples of Data Usage

Data analysis is no longer a special skill used only by companies.
It is seamlessly integrated into many of the services we use every single day.

For example, these services rely heavily on data analysis:

• Streaming apps recommend content you might enjoy
• Online shopping malls provide personalized product recommendations
• Banks monitor transactions to detect fraudulent activity

In this lesson, we'll use Python to walk through one of the most basic data analysis tasks step by step.

One of the most commonly used concepts in data analysis is the average.

The average is a fundamental indicator of the central tendency of a dataset and is widely used to summarize data.

For example, let's calculate the average number of steps per day using step count data from the past week.

The formula for calculating an average is simple:

Average = Sum of Data ÷ Number of Data Points

In Python, we can easily implement this using the built-in functions sum() and len().

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# Daily step counts collected from a health-tracking app
daily_steps = [8120, 9200, 7890, 10230, 8675, 9340, 10000]

# Calculate the total number of steps
total_steps = sum(daily_steps)

# Divide by the number of days to get the average
average_steps = total_steps / len(daily_steps)

# Print the result
print("Average daily steps:", int(average_steps))

Run the code block on the right to check the result.

You'll see the average daily steps calculated based on the weekly data.

For small datasets, sum() and len() are more than enough to calculate averages. However, when working with tens of thousands or even millions of records, libraries like NumPy and Pandas are commonly used for faster and more efficient calculations.

We'll cover how to use NumPy and Pandas for these tasks in later lessons. In the next lesson, we'll explore the difference between structured data and unstructured data.