Here is a list of some of the best web, mobile, and big data technologies for a programmer to learn in 2022. These technologies will make your resume more attractive help you take your career to the next level in the same job. It's essential to keep moving forward because if you are stuck, you won't grow, and learning new and popular technologies always breaks the shackles.

10 Frameworks & Libraries Developers Can Learn in 2022

Spring Boot makes it easy to create stand-alone, production-grade Spring-based Applications that you can "just run." Spring Boot takes an opinionated view of the Spring platform and third-party libraries so you can get started with minimum fuss.If you are a Java developer working on web development, then I strongly suggest you learn Spring Boot in 2022.

1. Spring Boot

React is a JavaScript library for building user interfaces. It's declarative: React uses a declarative paradigm that makes it easier to reason about your application.It's efficient: React computes the minimal set of changes necessary to keep your DOM up-to-date. And it's flexible: React works with the libraries and frameworks.In short, A declarative, dynamic, and flexible JavaScript library for building user interfaces.

2. React.js

Node.js is a JavaScript runtime built on Chrome's V8 JavaScript engine. It allows you to write a server-side application in JavaScript. Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient.If you learn Node.js and React, then you can write a client-server application in just one programming language, JavaScript.

3. Node.js

Angular is an open-source JavaScript framework, maintained by Google, that assists with running single-page applications. Its goal is to augment web-based applications with the model--view--controller (MVC) capability, to make both development and testing easier.It's very different from its predecessor Angular JS, which is outdated now, but learning Angular 2 or Angular 8 is still a great idea in 2022.

4. Angular

TensorFlow is one of the most popular machine learning APIs which allows you to automate several real-world tasks, for example, you can use it for image detection.It's a good idea to learn cutting-edge technology like Machine learning in 2022, and there is no better library to start with than TensorFlow.In fact, a Japanese farmer turned programmer used it to detect the best quality Cucumber, which only his father and mother can do with his years of experience.

5. TensorFlow

ASP.NET Core is a free and open-source web framework, and the next generation of ASP.NET, developed by Microsoft and the community. It is a modular framework that runs on both the full .NET Framework, on Windows, and the cross-platform.

6. .NET Core

It's all about some of the best web development, mobile, machine learning, and big data frameworks and libraries. Software developers can learn in 2022. 

These are the best frameworks a Software developer can learn in 2022 for app development, machine learning, web development, and backend development. Tagged with java, programming, development, react.

10 Frameworks Software Developers can learn in 2022

Becoming a Data Scientists is an exciting path, but you cannot learn data science within one year or six months—instead, it’s a lifetime process that you have to follow with proper dedication and hard work. 
To guide your journey, the skills outlined here are the first you must acquire to become a data scientist.

Becoming A Data Scientist

Python is the most preferred coding language and is adopted by most Data Scientists. It is easy to understand, versatile, and supports various in-built libraries such as Numpy, Pandas, MatplotLib, Seaborn, Scipy, and many more.<ul><li>FreeCodeCamp’s Python Tutorial (Recommended)</li><li>Kaggle’s Python Course</li><li>Krish Naik’s Python Tutorial (Recommended)</li><li>Udemy’s Python for Data Science and Machine Learning Bootcamp</li><li>Coursera Python Course</li></ul>While learning Python, one should know essential Python variables, data types, OOPs concepts, Numpy, Pandas, Matplotlib, and Seaborn.

Choose A Programming Language (Python / R)

For becoming a Data Scientist, having knowledge of statistics and probability is as essential as having salt in food. Knowing them will help the data scientists interpret large data sets, get insights from them, and analyze them better.<ul><li>Krish Naik’s Statistics Playlist (Recommended)</li><li>Coursera Statistics Course</li><li>Khan Academy Statistics And Probability Course</li><li>FreeCodeCamp Statistics Course (Recommended)</li></ul>

Knowledge Of Statistics And Probability

Structured Query Language (SQL) is used for extracting and communicating with large databases. One should focus on understanding the different types of normalization, writing nested queries, using co-related questions, group-by, performing join operations, etc., on the data and extract in raw format. This data will then further be cleaned either in Microsoft Excel or by using Python libraries.<ul><li>Freecodecamp SQL (Recommended)</li><li>Intro To SQL By Kaggle (Recommended)</li><li>Advanced SQL By kaggle</li><li>Edureka’s SQL Playlist</li></ul>

Learn SQL

When a Data Scientist is given a project, the majority of the time goes into cleaning the data set, removing unwanted values, handling missing values. It can be achieved by using some inbuilt python libraries like Pandas and Numpy.One should also know how to manipulate data using Microsoft Excel.<ul><li>Blog — Cleaning Data Using Python (Recommended)</li><li>Edureka’s Microsoft Excel Course</li><li>Learning Pandas By Kaggle (Recommended)</li></ul>

Data Cleaning

Exploratory data analysis is the essential part when talking about data science. The data scientist has many tasks, including finding data patterns, analyzing data, finding the appropriate trends in the data and obtaining valuable insights, etc., from them with the help of various graphical and statistical methods, including:A) Data Analysis using Pandas and NumpyB) Data ManipulationC) Data Visualization<ul><li>Intro To EDA By Code Heroku’s (Recommended)</li><li>Blog — Performing EDA on Iris Data Set (Recommended)</li><li>Coursera Course On EDA, Statistics, Probability</li></ul>

Exploratory Data Analysis

According to Google, “Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.”It is the most crucial step in a life cycle of a data scientist where one has to build various models using machine learning algorithms and should be able to predict and come with the most optimum solution to solve any problem.<ul><li>Machine Learning By Andrew NG (Recommended)</li><li>Deep Learning By Krish Naik</li></ul>

Learn Machine Learning Algorithms

After acquiring the basics of Data Science, now it’s time to get hands-on experience in its part. There are many online platforms, like Kaggle and Analytics Vidhya, that can provide you with hands-on experience with both beginner and advanced level data sets. They can help you to understand various machine learning algorithms, different analyzing techniques, etc.

Practice On Analytics Vidhya and Kaggle

Becoming a Data Scientists is an exciting path, but you cannot learn data science within one year or six months—instead, it’s a lifetime process that you have to follow with proper dedication and hard work. To guide your journey, the skills outlined here are the first you must acquire to…

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