DSA Intro
Introduction to Data Structures and Algorithms
The topic of data structures is the various ways that data can be organized and stored.
The study of algorithms involves finding solutions to various issues, frequently by navigating and modifying data structures.
The theory of data structures and algorithms, or DSA, enables us to effectively solve issues by utilizing vast volumes of data.
What are Data Structures?
Data can be stored using a data structure.
Depending on the type of data we have and our goals for it, we organize it differently.
To get the notion, let’s first look at an example that doesn’t use computers.
A family tree is used as the data structure when storing information about individuals who are connected to us. We want an overview so that we can quickly locate a certain family member, several generations back, and we have information about the people we are related to and how they are related, so we decided to utilize a family tree as the data structure.
When one is presented with a family tree data structure like this, it becomes effortless to determine, for instance, the identity of my mother’s mother—It’s ‘Emma,’ right? But it would be challenging to ascertain the people’ relationships if this data structure hadn’t provided the ties from child to parents.
With the help of data structures, we can effectively handle massive volumes of data for applications like internet indexing services and enormous databases.
Building quick and effective algorithms requires the use of data structures. They facilitate data management and organization, simplify processes, and boost productivity.
There are two distinct types of data structures in computer science.
Programming languages include primitive data structures, which are simple data structures that are used to represent single values such characters, integers, floating-point numbers, and booleans.
Higher-level data structures known as abstract data structures offer more intricate and specialized operations and are constructed using simple data types. Abstract data structures are frequently represented by trees, graphs, queues, stacks, linked lists, arrays, and stacks.
What are Algorithms?
An algorithm is a collection of sequential instructions used to solve a particular problem or accomplish a certain objective.
An example of an algorithm is a cooking recipe put down on paper, with the objective being to prepare a specific supper. The exact steps required to prepare a particular dinner are given.
In computer science, an algorithm is a set of sequential instructions defined in a programming language that uses data structures in place of components in a recipe.
Since algorithms give tasks step-by-step instructions, they are essential to computer programming. A fast program can be created from a sluggish one by utilizing an efficient algorithm to assist us find the solution we need.
Developers can create more effective applications by learning about algorithms.
Algorithm examples:
- Finding the fastest route in a GPS navigation system
- Navigating an airplane or a car (cruise control)
- Finding what users search for (search engine)
- Sorting, for example sorting movies by rating
The algorithms that we will examine in this course are generally built to operate on certain data structures and are intended to solve particular problems. For instance, the ‘Bubble Sort’ method is intended to work with arrays and is used to sort values.
Data Structures together with Algorithms
Algorithms and data structures (DSA) work together closely. In the same way, without a data structure to deal with, the methods in this course are not very useful for searching through or manipulating data structures efficiently.
DSA is the process of identifying effective methods for data storage and retrieval, data processing, and issue solving.
Knowing DSA allows you to:
- Decide which data structure or algorithm is best for a given situation.
- Make programs that run faster or use less memory.
- Understand how to approach complex problems and solve them in a systematic way.
Where is Data Structures and Algorithms Needed?
From operating systems to web apps, almost all software systems use data structures and algorithms (DSA):
- for controlling vast volumes of data, like in a search engine or social network.
- To choose which task a computer should perform first when scheduling tasks.
- To plan a route, use a GPS device to determine the quickest route between points A and B.
- for streamlining procedures, like allocating work to be finished as soon as feasible.
- For resolving complicated issues: From figuring out how to load a truck optimally to teaching a machine to “learn” from information.
DSA is essential to almost every aspect of the software industry:
- Operating Systems
- Database Systems
- Web Applications
- Machine Learning
- Video Games
- Cryptographic Systems
- Data Analysis
- Search Engines
Theory and Terminology
To better comprehend the data structures and algorithms we will be working on, new theoretical ideas and vocabulary (new words) will be required as we proceed through this course.
When necessary, these new terms and concepts will be introduced and fully explained, but in the meanwhile, here is a list of some essential terms to give you a general idea of what to expect:
| Term | Description |
|---|---|
| Algorithm | A set of step-by-step instructions to solve a specific problem. |
| Data Structure | A way of organizing data so it can be used efficiently. Common data structures include arrays, linked lists, and binary trees. |
| Time Complexity | A measure of the amount of time an algorithm takes to run, depending on the amount of data the algorithm is working on. |
| Space Complexity | A measure of the amount of memory an algorithm uses, depending on the amount of data the algorithm is working on. |
| Big O Notation | A mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity. Used in this tutorial to describe the time complexity of an algorithm. |
| Recursion | A programming technique where a function calls itself. |
| Divide and Conquer | A method of solving complex problems by breaking them into smaller, more manageable sub-problems, solving the sub-problems, and combining the solutions. Recursion is often used when using this method in an algorithm. |
| Brute Force | A simple and straight forward way an algorithm can work by simply trying all possible solutions and then choosing the best one. |
Where to Start?
Before going on to the next data structure in this course, you will first learn about one with corresponding algorithms.
Since the concepts get more complicated as the training progresses, it’s best to understand DSA by following the tutorial step-by-step from the beginning.
Additionally, before beginning this lesson, you should be proficient in at least one of the most widely used programming languages, such as Python, C, or JavaScript, as stated on the preceding page.
The next page will examine two distinct algorithms that use only simple data structures—two integer variables—to output the first 100 Fibonacci values. There are two types of algorithms: one that employs a loop and the other that uses recursion.
