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Algorithms and Data Structures Tutorial - Full Course for Beginners
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Algorithms and Data Structures Tutorial - Full Course for Beginners

Introduction to Algorithms

This course aims to dispel the fear and mystery surrounding algorithms. It covers the basic set of knowledge needed as a foundation for learning about algorithms, including tools for evaluating, understanding, comparing, and making statements about their utility in different contexts. The course is less about specific algorithms and more about the concepts behind them.

What is an Algorithm?

  • An algorithm is a set of steps or instructions for completing a task.
  • In computer science, an algorithm specifically refers to the set of steps a program takes to finish a task.
  • Writing code generally involves writing an algorithm.

Why Learn About Algorithms?

  • Algorithms are common problems in computer science that have multiple solutions.
  • Learning about algorithms helps you develop better problem-solving skills and come up with more efficient solutions.

Course Goals

  • The goal of this course is to provide a foundation for learning about algorithms by covering basic concepts such as evaluating, understanding, comparing, and making statements about their utility in different contexts.
  • The course will use well-known algorithms to teach these concepts and will involve writing code in Python.

Prerequisites

  • Some programming experience is required to follow along with this course.
  • If you don't know how to code or if you know how to code in a different language than Python, check out the notes section of this video for links to other content that might be useful.

Introduction to Algorithms and Algorithmic Thinking

This section introduces the importance of understanding algorithms and algorithmic thinking. It explains that having a deeper understanding of complexity and efficiency in programming is crucial for developing better code.

Understanding Algorithms

  • If you're unaware that a solution exists, you might try to come up with one yourself, but there's a likelihood that your solution won't be as good or efficient compared to those that have been thoroughly reviewed.
  • Understanding when to apply an algorithm requires properly understanding the problem at hand.
  • Algorithmic thinking is the ability to break down a problem into distinct steps and identify which algorithm or data structure is best for the task at hand.

Importance of Learning Algorithms

  • Learning about algorithms gives you a deeper understanding of complexity and efficiency in programming.
  • Having a better sense of how your code will perform in different situations is something that you'll always want to develop and hone.
  • Algorithmic thinking is why algorithms also come up in big tech interviews.

Playing a Game: Introduction to Problem Solving Strategies

This section uses a game as an analogy for problem-solving strategies. It highlights the importance of defining the problem set clearly before attempting to solve it.

The Game

  • The game involves guessing a number between 1 and 10, with the winner being the one who guesses correctly with the fewest tries.
  • When guessing, players are told if their guess is too high or too low.

Defining the Problem Set

  • An important part of algorithmic thinking is to clearly define what the problem set is and clarify what values count as inputs.
  • No solution works on every problem, so we should try and figure out what solution works better for each specific problem.

Different Approaches

  • With any given problem, there's no one best solution.
  • Different approaches to figuring out a solution can yield different results.
  • When the number was larger, we start to see that one player's strategy did better than the other.

Conclusion

This section concludes the video by summarizing the importance of understanding algorithms and algorithmic thinking. It emphasizes that there is no one best solution for any given problem and that different approaches can yield different results.

Key Takeaways

  • Understanding algorithms and algorithmic thinking is crucial for developing better code.
  • Algorithmic thinking involves breaking down a problem into distinct steps and identifying which algorithm or data structure is best for the task at hand.
  • Clearly defining what the problem set is and clarifying what values count as inputs is an important part of algorithmic thinking.
  • There's no one best solution for any given problem, and different approaches can yield different results.

Algorithmic Thinking

In this section, the speaker discusses an exercise where his co-workers had to guess a number he was thinking of. He compares the different approaches taken by two co-workers, John and Brittany, to find the number and explains how this exercise is an example of real-life situations that developers face when building websites and apps.

Different Approaches to Searching for a Value

  • The speaker runs through the guessing game again, this time with a range of numbers from 1 to 100.
  • John takes five tries to guess when the answer is at the start of the range (i.e., 5), while Brittany takes seven tries.
  • When the answer is at the end of the range (i.e., 100), John takes 100 tries, while Brittany takes only seven tries.
  • The speed at which you find something really matters in real-life situations. For example, if you're searching for someone on Facebook, it has to search across billions of records and find that person as quickly as possible.

Linear Search vs. Binary Search

  • John's approach is an example of linear search or sequential search. It involves starting at one end of a list and checking each item until you find what you're looking for.
  • Brittany's approach is an example of binary search. It involves dividing a sorted list into halves repeatedly until you narrow down your search to just one item.

Conclusion

  • The speaker concludes that there are many ways to solve problems like searching for values in real-life situations. Developers need to choose algorithms that are efficient and fast enough for their specific needs.

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Video description

In this course you will learn about algorithms and data structures, two of the fundamental topics in computer science. There are three main parts to this course: algorithms, data structures, and a deep dive into sorting and searching algorithms. By the end, you will understand what algorithms and data structures are, how they are measured and evaluated, and how they are used to solve problems. This course was developed by Pasan Premaratne and Jay McGavren. It was made possible by a grant from teamtreehouse.com ❤️ Try interactive Algorithms courses we love, right in your browser: https://scrimba.com/freeCodeCamp-Algorithms (Made possible by a grant from our friends at Scrimba) ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Introduction to Algorithms ⌨️ (1:57:44) Introduction to Data Structures ⌨️ (4:11:02) Algorithms: Sorting and Searching ⭐️ Code Snippets for Course ⭐️ 💻 Introduction to Algorithms: ⌨️ Algorithms in Code: 🔗 Linear Search Implementations: https://teamtreehouse.com/library/introduction-to-algorithms/algorithms-in-code/linear-search-implementations 🔗 Binary Search Implementations: https://teamtreehouse.com/library/introduction-to-algorithms/algorithms-in-code/binary-search-implementations 💻 Introduction to Data Structures ⌨️ Exploring Arrays: 🔗 Array Characteristics and Storage: https://teamtreehouse.com/library/introduction-to-data-structures/exploring-arrays/array-characteristics-and-storage 🔗 Operations on Arrays: https://teamtreehouse.com/library/introduction-to-data-structures/exploring-arrays/operations-on-arrays ⌨️ Building a Linked List: 🔗 Singly and Doubly Linked Lists: https://teamtreehouse.com/library/introduction-to-data-structures/building-a-linked-list/singly-and-doubly-linked-lists-2 🔗 Linked List Operations: https://teamtreehouse.com/library/introduction-to-data-structures/building-a-linked-list/linked-lists-operations ⌨️ The Merge Sort Algorithm: 🔗 Merge Sort Implementations: https://teamtreehouse.com/library/introduction-to-data-structures/the-merge-sort-algorithm/merge-sort-implementations 🔗 Alternate Versions of Merge Sort: https://teamtreehouse.com/library/introduction-to-data-structures/the-merge-sort-algorithm/alternate-versions-of-merge-sort ⌨️ Merge Sort and Linked Lists: 🔗 Implementing Merge Sort on Linked Lists: https://teamtreehouse.com/library/introduction-to-data-structures/merge-sort-and-linked-lists/implementing-merge-sort-on-linked-lists 💻 Algorithms: Sorting and Searching ⌨️ Sorting Algorithms: 🔗 Code for Bogosort: https://teamtreehouse.com/library/algorithms-sorting-and-searching/sorting-algorithms/code-for-bogosort 🔗 Code for Selection Sort: https://teamtreehouse.com/library/algorithms-sorting-and-searching/sorting-algorithms/code-for-selection-sort 🔗 Code for Quicksort: https://teamtreehouse.com/library/algorithms-sorting-and-searching/sorting-algorithms/code-for-quicksort 🔗 Code for Merge Sort: https://teamtreehouse.com/library/algorithms-sorting-and-searching/sorting-algorithms/code-for-merge-sort ⌨️ Searching Names: 🔗 Code for Linear Search: https://teamtreehouse.com/library/algorithms-sorting-and-searching/searching-names/code-for-linear-search 🔗 Code for Binary Search: https://teamtreehouse.com/library/algorithms-sorting-and-searching/searching-names/code-for-binary-search -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news