Table of contents

Read No.	Name of chapter
1	Pain vs. Suffering
1	Beginners Guide to Big O notation

Pain vs. Suffering

The pain that you’ll experience during this course is no exception; most times it won’t feel good. You won’t feel good.

• You’ll be pushed mentally, having to think your way through problems that you had only even heard about a few hours beforehand.

• You’ll have to forge your own path toward a solution.

• You’ll have to research for hours on end, fleshing out the skeleton that we discuss in class, piling information on top of application so that you can reach your goals.

• You’ll be pushed emotionally, constantly confronted with your own lack of understanding of a topic.

• You’ll Constantly be having to figure out how to collaborate with new people and deal with their (and your own) emotional quirks.

• You’ll Constantly be thrust far outside of your comfort zone with the expectation that you’ll survive for the next push forward.

• You’ll Constantly be dealing with uncertainty of what awaits you at the end of the 10 weeks, and whether or not you’ll make it.

• You’ll be pushed physically, and while sitting in a chair and staring at your screen isn’t the most strenuous exercise in the world, the consecutive hours of it will take its toll.

• You’ll lose sleep, you’ll forget to work out, and you’ll feel exhausted all while being filled with information day after day.

All growth comes with some degree of pain, as it pulls you out of your comfort zone. The greater the growth, the greater the pain. But pain in the service of growth is a good thing, as long as that pain is what’s necessary to achieve the growth that you’re aiming for. And even better than that, this pain is only temporary. It’s what will launch you forward into the next phase of your life.

Suffering is pain without purpose. Pain with no higher goal. Pain with no dreams, no ambition, no aspiration.

Some Advices:

As you experience pain, seek the remedy! Ask questions that bridge the gap between what you know and what you need to be able to do. Research! Build your resources and your community! Don’t experience the pain in silence—that slides toward needless pain and the realm of suffering.

Find the common thread that makes the pain worthwhile, that puts the pain into perspective. You’re here because you chose to invest in a different life. A better life.

You’re in this class so that you can become not just a competent Python developer, but a spectacular craftsman of software with Python as your tool. The pain that you feel? Those are the callouses that you build while learning to use that tool.

It’s not easy, nor is it common. This is why such skilled people are highly-valued in the industry. You are building your value. You are experiencing the pain of growth. Live it, love it, and take it forward into your future as a full-stack software developer capable of meeting all challenges with a clear head and strong heart.

Beginners Guide to Big O notation

Big O notation is used in Computer Science to describe the performance or complexity of an algorithm. Big O specifically describes the worst-case scenario, and can be used to describe the execution time required or the space used (e.g. in memory or on disk) by an algorithm.

Below are some common orders of growth along with descriptions and examples where possible.

O(1)

O(1) describes an algorithm that will always execute in the same time (or space) regardless of the size of the input data set.

bool IsFirstElementNull(IList<string> elements)
{
    return elements[0] == null;
}

O(N)

O(N) describes an algorithm whose performance will grow linearly and in direct proportion to the size of the input data set. The example below also demonstrates how Big O favours the worst-case performance scenario; a matching string could be found during any iteration of the for loop and the function would return early, but Big O notation will always assume the upper limit where the algorithm will perform the maximum number of iterations.

bool ContainsValue(IList<string> elements, string value)
{
    foreach (var element in elements)
    {
        if (element == value) return true;
    }

    return false;
}

O(N2)

O(N2) represents an algorithm whose performance is directly proportional to the square of the size of the input data set. This is common with algorithms that involve nested iterations over the data set. Deeper nested iterations will result in O(N3), O(N4) etc.

bool ContainsDuplicates(IList<string> elements)
{
    for (var outer = 0; outer < elements.Count; outer++)
    {
        for (var inner = 0; inner < elements.Count; inner++)
        {
            // Don't compare with self
            if (outer == inner) continue;

            if (elements[outer] == elements[inner]) return true;
        }
    }

    return false;
}

O(2^N)

O(2^N) denotes an algorithm whose growth doubles with each additon to the input data set. The growth curve of an O(2^N) function is exponential - starting off very shallow, then rising meteorically. An example of an O(2^N) function is the recursive calculation of Fibonacci numbers:

int Fibonacci(int number)
{
    if (number <= 1) return number;

    return Fibonacci(number - 2) + Fibonacci(number - 1);
}

Logarithms

Binary search is a technique used to search sorted data sets. It works by selecting the middle element of the data set, essentially the median, and compares it against a target value. If the values match it will return success. If the target value is higher than the value of the probe element it will take the upper half of the data set and perform the same operation against it. Likewise, if the target value is lower than the value of the probe element it will perform the operation against the lower half. It will continue to halve the data set with each iteration until the value has been found or until it can no longer split the data set.

This type of algorithm is described as O(log N). The iterative halving of data sets described in the binary search example produces a growth curve that peaks at the beginning and slowly flattens out as the size of the data sets increase e.g. an input data set containing 10 items takes one second to complete, a data set containing 100 items takes two seconds, and a data set containing 1000 items will take three seconds. Doubling the size of the input data set has little effect on its growth as after a single iteration of the algorithm the data set will be halved and therefore on a par with an input data set half the size. This makes algorithms like binary search extremely efficient when dealing with large data sets.