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Commit 2d1470a

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‎book/content/part01/algorithms-analysis.asc‎

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@@ -28,15 +28,14 @@ Before going deeper into space and time complexity, let's cover the basics real
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Algorithms (as you might know) are steps of how to do some tasks. When you cook, you follow a recipe (or an algorithm) to prepare a dish. Let's say you want to make a pizza.
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.Example of an algorithm
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.Example of an algorithm to make pizza
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[source, javascript]
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----
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import { punchDown, rollOut, applyToppings, Oven } from '../pizza-utils';
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import { rollOut, applyToppings, Oven } from '../pizza-utils';
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function makePizza(dough, toppings = ['cheese']) {
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const oven = new Oven(450);
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const punchedDough = punchDown(dough);
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const rolledDough = rollOut(punchedDough);
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const rolledDough = rollOut(dough);
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const rawPizza = applyToppings(rolledDough, toppings);
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const pizzaPromise = oven.bake(rawPizza, { minutes: 20 });
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return pizzaPromise;

‎book/content/part01/how-to-big-o.asc‎

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@@ -111,6 +111,7 @@ T(n) = n * [t(statement 1) + m * t(statement 2...3)]
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Assuming the statements from 1 to 3 are `O(1)`, we would have a runtime of `O(n * m)`.
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If instead of `m`, you had to iterate on `n` again, then it would be `O(n^2)`. Another typical case is having a function inside a loop. Let's see how to deal with that next.
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[[big-o-function-statement]]
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*Function call statements*
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When you calculate your programs' time complexity and invoke a function, you need to be aware of its runtime. If you created the function, that might be a simple inspection of the implementation. However, if you are using a library function, you might infer it from the language/library documentation.
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‎book/part02-linear-data-structures.asc‎

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Data Structures comes in many flavors. There’s no one to rule them all. You have to know the tradeoffs so you can choose the right one for the job.
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Even though in your day-to-day, you might not need to re-implementing them, knowing how they work internally would help you know when to use one over the other or even tweak them to create a new one. We are going to explore the most common data structures' time and space complexity.
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Even though in your day-to-day, you might not need to re-implementing them, knowing how they work internally would help you to calculate the time complexity of your code (Remember the chapter <<big-o-function-statement, How to determine Big-O from code?>>).
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Also, when you are aware of the data structures implementations, you spot when to use one over the another or even extend them to create a new one. We are going to explore the most common data structures' time and space complexity.
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.In this part we are going to learn about the following linear data structures:
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- <<array-chap>>
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endif::[]
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<<<
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include::content/part02/array.asc[]
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<<<
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<<<
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include::content/part02/array-vs-list-vs-queue-vs-stack.asc[]
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