Programs
Programs
Data Science
Python has been around since the late 1980s, but it’s never been more popular than today. It can be challenging for a new programmer or programmer looking to learn Python to determine the best way to get started with the language.
To get up and running quickly, you’ll need a solid understanding of what the language does, what it’s useful for, and how to use it to meet your programming needs.
This beginner’s guide to using Python will help you understand how Python compares to other languages, where you can use it, and some of the situations where Python isn’t ideal so that you can decide whether or not it will work best for your programming needs.
Python is a general-purpose, high-level programming language that supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles.
Python has been extensively used in academia and industry, and some of its qualities include a very clean syntax and its use of whitespace indentation instead of curly braces to delimit blocks, both of which make it easy to learn and read code.
There are a lot of uses for it! Its most common use is as a scripting language, and it works great in smaller projects with limited features and power. It’s also relatively simple to learn, so even those without coding experience can also use it.
These factors make it ideal for quick or medium-sized tasks that don’t require a lot of time investment. If you need something done quickly, Python might be your best bet.
Another widespread use of Python in data analysis. Its ability to parse text and manipulate data makes it very useful in these situations. If you need more information about specific parts of your project, Python can help find answers by collecting data on them automatically (if needed).
Some other uses include
Recommended: Python Training Institute in Faridabad
Python has also become a hugely popular data science tool, with an enormous number of libraries dedicated to solving any number of numerical and statistical problems.
This makes it ideal for creating and analyzing datasets, so Python could be a great choice if you’re interested in data analysis or artificial intelligence.
It’s also worth noting that many popular machine learning and deep learning frameworks have been built using Python. Python may be a good place to start if you’re looking to get into these fields.
Additonal Read: How to Fix ValueError?
At its core, Python can be used as a general-purpose programming language, allowing you to do almost anything you want with it. You might choose to use it in a traditional development role, like writing scripts or tools that automate processes within your organization.
You might even decide to build a full-fledged application on top of it. But beyond that, some specific industries have latched onto Python in recent years, and here are some of them and how they use Python.
There’s been a rise in enterprise software being written using Python. It’s easy for companies to get behind because it provides scalability, flexibility, and speed to market at an attractive price point. Since it allows for so much value at scale, big players like Google and Amazon have adopted it into their stacks (Google uses their variant Go).
As data science matures as an industry, we see more businesses adopting open-source frameworks, essentially where Python shines!
For Designing Web Apps
Because of its simple syntax and readability, it’s not surprising that developers consider Python ideal for creating websites and web apps. Learning how to code in Python is easy. Compared to other programming languages like C or C++, there are very few exceptions to the rules.
Moreover, you don’t need a lot of specialized knowledge because it has fewer features than other coding languages do. This makes it easier to pick up, especially if you’re new to coding.
It also means that once you know how to program in Python, you can use your skills on any platform
While Python is perhaps most widely known as a general-purpose programming language (GPPL), it’s also commonly used in more specialized fields. In particular, data scientists increasingly turn to Python when building AI systems.
Several critical aspects of Python appeal to data scientists: Besides providing programmers with immense flexibility, modern versions of Python are highly portable; many developers agree that no other GPPL comes close to matching its combination of portability and readability.
And then there’s speed which makes sense, given that Guido van Rossum developed Python as an extension of his earlier project, ABC. Finally, Python offers excellent support for machine learning, including several frameworks designed specifically for machine learning tasks.
If you have a passion for building games—from puzzles to role-playing to first-person shooters—you might find yourself at home with Python. This language is great if you need to write simple game engines or tools that help you build better games.
And it’s also been around long enough to have libraries available for just about any game genre under the sun. Whether you want to make something like Super Mario Bros., Pac-Man, or an MMORPG (massively-multiplayer online role-playing game).
There are plenty of open-source frameworks that can help you out. One of these frameworks even has its own graphics engine based on OpenGL called PyGame.
Many other gaming languages are C/C++ based but use Python as their scripting language due to its flexibility and portability across platforms.
Conclusion
Python is a universal language. If you’re starting in data science or web development, certain aspects of Python will make your journey more accessible and more fun.
At its core, though, it’s a powerful general-purpose programming language capable of accomplishing just about anything you can dream up.
By following along with these tutorials on applications for Python, I hope to inspire you to learn more about coding and Python itself. Happy learning!
Start Learning Now
Useful Links
© Copyright 2020 Dreamer Infotech | All rights reserved.
MySQL JOIN
DATA PRE-PROCESSING/WRANGLING
Event listeners and handlers
Mouse events
Keyboard events
Accessing and manipulating HTML elements
DOM navigation and manipulation
Creating and removing HTML elements
What is React.js?
History and evolution of React.js
React.js components and their role in
building web applications
Setting up the development environment
Introduction to JSX
Basic syntax and rules of JSX
Embedding expressions in JSX
Conditional rendering in JSX
Working with lists in JSX
Understanding components
Creating class components
Creating functional components
Props and state in React components
Lifecycle methods in React components
Handling events in React
Binding event handlers in React
Passing data to event handlers in React
Conditional rendering based on events in
React
Creating forms in React
Handling form submission in React
Controlled and uncontrolled components
in React
Introduction to React Router
Setting up routing in React
Creating routes in React
What is MongoDB?
Advantages of MongoDB over RDBMS
MongoDB data model and architecture
Setting up the development environment
Creating and inserting documents in
MongoDB
Reading documents from MongoDB
Updating documents in MongoDB
Deleting documents from MongoDB
Querying MongoDB using find() method
Querying MongoDB using comparison and
logical operators
Querying MongoDB using regular
expressions
Querying MongoDB using aggregation
framework
What is Node.js?
Advantages of Node.js over other serverside technologies
Node.js architecture and event-driven
programming model
Setting up the development environment
Introduction to Node.js modules
Creating and using built-in modules
Creating and using custom modules
Working with NPM (Node Package
Manager)
Creating HTTP server in Node.js
Understanding HTTP methods and status
codes
Handling HTTP requests and responses
Serving static files in Node.js
Introduction to Express.js framework
Creating and configuring Express.js
application
Handling HTTP requests and responses in
Express.js
Implementing middleware in Express.js
Introduction to database connectivity in
Node.js
Connecting to MongoDB database in
Node.js
Performing CRUD operations in Node.js
using MongoDB
Using Mongoose ORM with MongoDB in
Node.js
Introduction to security in Node.js
Understanding and implementing
authentication and authorization in
Node.js
Implementing HTTPS in Node.js
Preventing common security vulnerabilities
in Node.js
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Optio, neque qui velit. Magni dolorum quidem ipsam eligendi, totam, facilis laudantium cum accusamus ullam voluptatibus commodi numquam, error, est. Ea, consequatur.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Optio, neque qui velit. Magni dolorum quidem ipsam eligendi, totam, facilis laudantium cum accusamus ullam voluptatibus commodi numquam, error, est. Ea, consequatur.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Optio, neque qui velit. Magni dolorum quidem ipsam eligendi, totam, facilis laudantium cum accusamus ullam voluptatibus commodi numquam, error, est. Ea, consequatur.