1.2: What is programming?

Programming, at its core, is the act of instructing a computer to perform specific tasks or solve problems. Despite the astonishing computational prowess of modern computers, they are fundamentally rule-followers. They rely on precise, step-by-step instructions provided by humans—programmers—to carry out their tasks.

This process is analogous to cooking using a recipe. To make a dish, you don't just throw all the ingredients into a pot at once. Instead, you follow a sequence of steps, each with a specific order and set of actions—like chopping the vegetables, sautéing them, adding spices, and so on.

In programming, the problem you want to solve is the dish you want to cook, and your program is the recipe. Just as a recipe breaks down the complex process of cooking a dish into a series of manageable steps, programming involves breaking down a problem into smaller, logical steps that a computer can understand and execute.

For example, if you're programming a weather forecasting model, you don't tell the computer to "predict tomorrow's weather." Instead, you might instruct it to first gather data from various weather sensors, then clean and organize that data, perform specific calculations using that data (like calculating pressure gradients, humidity levels, temperature changes), apply certain algorithms or models, and finally output a prediction for the next day's weather. Each of these steps involves its own series of sub-steps, which are coded into the program.

Therefore, the art of programming isn't just about writing code—it's about logical thinking, problem-solving, and breaking complex problems into smaller, manageable tasks that a computer can execute. It's a vital skill in many fields, including atmospheric sciences, where complex data analysis and computational modeling are key to understanding and predicting weather and climate patterns.

As an example, imagine that you're calculating the number of heads you get when flipping a coin 100 times. Here's how we can break this task down into manageable programming steps:

1. Define the Process: A single coin flip can result in either heads or tails. We'll need to define this process in our program, which usually involves generating a random number that represents either heads or tails. In Python, we can use the `random` module for this.

2. Simulate the Flips: Once we have the coin flip defined, we need to repeat this process 100 times. This is typically done with a loop that runs the coin flip process as many times as we want.

3. Count the Heads: Each time we flip the coin, we need to check if the result was heads and, if so, increment a count of heads. This typically involves an `if` statement to check the result of each flip, and a variable to store the count.

4. Output the Result: After the loop has run 100 times, we should have the total number of heads. The final step is to output this result, which might be as simple as printing the value to the console.

In summary, the problem can be broken down into defining the random process (a coin flip), simulating this process a given number of times (100 coin flips), counting the desired outcomes (number of heads), and outputting the result. Each of these steps can be translated into specific programming commands or code blocks.

At its core, being a proficient programmer requires the ability to take complex problems and decompose them into simpler, more manageable steps. This skill, often called computational thinking, is the backbone of efficient problem solving in the realm of programming. It involves taking the full scope of a problem, understanding its intricacies, and methodically breaking it down into sub-problems that are easier to handle. This step-by-step process allows you to tackle each part individually, and when all the parts are completed, the overall problem is solved. Therefore, honing your ability to segment problems is fundamental to successful programming.