Data structure and algorithms are standard terms that you must have heard if you have a computer science background. These terms are not specific to one programming language but form a part of almost all programming languages. Data structures and algorithms using python have multiple applications but more significantly in data science and machine learning.
Well, python is a highly popular programming language. As the language is pretty easy to learn and has a massive range of applications, more and more people are trying to learn this language. If you are new to programming languages, you would be interested in knowing more about them and their applications. This post will walk you through the concepts of data structures and algorithms in python.
A data structure is defined as a collection of data of similar types sequentially stored in a linear order. The data is stored and organized to make it easy to access and modify the data as and when required. In some instances, a data structure is the only thing that helps implement a particular data type. There are certain data types like:
Composite Data Type
A composite data type is a data structure composed of a scalar or another hybrid kind of data. It is used for problem solving with algorithms and data structures using python.
Abstract Data Type
An abstract type is defined as a set of behaviors for which a certain kind of data structure can be put to use as the best form of implementation for that particular data type.
There are four types of data structures:
An algorithm is a step-by-step process or a set of rules that one needs to follow to complete a specific task while solving a particular problem. Algorithms are ubiquitous. It is effortless to understand what an algorithm means. Algorithms are used in our daily life as well. Wonder how?
Well, if you are trying to bake a cake or cook something new, you are following a step-by-step process. So, we can say that this is a simple example of an algorithm. Algorithm programming is about writing down a set of rules which a computer can follow to perform a specific task. We can say that a program is like an algorithm that tells the computer what steps the computer needs to follow to act. You need to ask the computer to perform tasks because it is, after all, a machine and cannot do anything on its own.
There are different algorithms based on their concepts to get a task done. We know there are many kinds of algorithms, but the most fundamental kinds of computer science algorithms are as follows:
Divide and conquer algorithm: All you need to do is divide the problem into more minor issues of the same kind and solve each of these. Once done, combine all of them to solve the entire problem.
Brute force algorithm: You need to try all kinds of solutions until a proper solution is found.
Randomized algorithms: You need to solve the lowest and the most straightforward kind of problem before solving more significant problems until and unless the computation to find a solution to the problem is achieved.
Greedy algorithm: This is based on finding an optimal solution for a short part of a problem with the primary intent of finding an optimal solution for the whole problem.
Recursive Algorithms: One needs to solve the simplest form before solving any increasingly more significant versions of a problem until a proper solution is found.
Backtracking Algorithms: All you need to do is divide the problem into smaller subsections. Each of these can be attempted to be solved. But if the proper solution is not found, you need to move back in the problem until and unless a path is found that can help move forward to the next step.
Dynamic programming algorithms: All you need to do is break down a complex problem into a small collection of sub-problems. Next, you need to solve each subsection only once while storing their solutions for future use instead of computing the solutions again.
Did you know python has created Netflix's recommendation algorithm for the software in self-driving cars? Well, yeah. What else could be the reason for so much popularity? Why has this language suddenly become so common and popular?
Python is a general language that means it is designed to be used in many applications, including data science, web development, automation, and more. One needs to know python for data science. The language is highly versatile and very easy to learn. Python has been adopted by many who are not from a technical background. They are accountants, scientists who use the language for their regular tasks such as organizing finances, analyzing finance, etc. You should take up a data science with python course.
Python has a vast application in data science as professionals use this language to conduct statistical calculations and visualizations, build algorithms and analyze and manipulate data. You need to know about python for data science training. Python can be used to build a wide range of data visualizations, such as line, bar graphs, pie charts, and even histograms. It also has many libraries, making it easier for coders to write programs on various domains.
Here are some of the best jobs you can try if you have a degree in python. These are:
Python, a general-purpose software, is suitable for all software development.
1. Desktop GUI Applications - Python provides a TK GUI library that helps to develop a smooth user interface. The term GUI stands for Graphical User Interface. Some of the standard and popular GUI libraries are as follows - Tkinter, kivy, Pyside, etc
2. Web Applications - Python, can be used to develop web applications. It has a wide range of libraries that can help handle the internet protocols like HTML, XML, Email processing, etc. The Python web framework such as Django is popular on Instagram. Python has a wide range of valuable frameworks such as the following:
- Django and Pyramid framework.
- Plone and Django CMS.
3. Scientific and Numeric - Well, this is the time of Artificial Intelligence. In this case, a machine can perform various tasks similar to a human. Python is one of the most suitable languages for AI or even machine learning. It has different scientific and mathematical libraries that make calculations simple. The process of implementing machine algorithms is pretty complex. Python has a wide range of libraries for scientific and numeric purposes like Numpy, Scipy, and even Pandas.
Data structures and algorithms using python are interrelated to python, just like it plays a vital role in any other primary programming language. You can easily get data science with python certification that will help you know about both languages.
This post has thorough detail of all topics relevant to python and its wide range of applications.
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