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Practice

Everyday Examples of Data Usage

Data analysis is no longer a special skill used only by companies.
It's now an integral part of the various services we use every day, often working behind the scenes without us even noticing.

For example, the following services rely heavily on data analysis:

  • Streaming apps recommending content you might like
  • Online shopping platforms offering personalized product suggestions
  • Banks monitoring transactions to detect fraud

In this lesson, we'll use Python to practice one of the most basic data analysis tasks through hands-on exercises.


Python Warm-up: Calculating the Average​

One of the most common concepts in data analysis is the average.

The average is a basic metric used to represent the central tendency of a dataset and is widely used to summarize data.

For example, let's calculate the average daily sales amount using price data from items sold over a week.

The formula for calculating the average is straightforward:

Average = Total sum of data ÷ Number of data points

In Python, you can easily implement this calculation using the built-in functions sum() and len():

Python: Calculating Average Daily Sales
# Weekly sales data retrieved from an internal database
sales_data = [8120, 9200, 7890, 10230, 8675, 9340, 10000]

# Calculate total sales
total_sales = sum(sales_data)

# Divide by the number of days to get the average
average_sales = total_sales / len(sales_data)

# Print the result
print("Average daily sales:", int(average_sales))

Checking the Result​

Run the code on the right to check the result.

For small datasets, you can easily calculate the average using sum() and len(). However, when working with tens of thousands or even millions of data points, it's more efficient to use libraries like NumPy or Pandas for faster computations.

We'll explore how to leverage NumPy and Pandas in detail later. In the next lesson, we'll dive into the difference between structured data and unstructured data.