Welcome to today's lesson! Our topic for the day is data aggregation, a crucial aspect of data analysis. Like summarizing a massive book into key points, data aggregation summarizes large amounts of data into important highlights.

By the end of today, you'll be equipped with several aggregation methods to summarize data streams in C++. Let's get started!

Let's say we have an array of integers denoting the ages of a group of people. We will demonstrate several basic aggregation methods using C++'s built-in functions:

Here's a brief overview of the above code snippet:

1. Number of people: Uses ages.size() to get the number of elements in the vector.
2. Total age: Uses std::accumulate from the <numeric> header to sum all elements in the vector.
3. Youngest age: Uses std::min_element from the <algorithm> header to find the smallest element.
4. Oldest age: Uses std::max_element from the <algorithm> header to find the largest element.
5. Average age: Calculates the average age by dividing the total age by the number of people.
6. Age range: Computes the range of ages by subtracting the youngest age from the oldest age.

These functions provide essential aggregation operations and are widely used with data streams.

For deeper analysis, such as calculating the mode or most frequent age, we can use for and while loops.

For example, using for loops, we can find the mode or most frequent age:

while loops can also be used similarly for complex tasks.

Fantastic! You've just learned how to use basic and advanced data aggregation methods in C++. These techniques are pivotal in data analysis and understanding. Now, get ready for the practical tasks lined up next. They'll reinforce the skills you've just gained. Remember, the more you practice, the better you become. Good luck with your practice!