Developing a modern web application that covers the needs of a specific business can be time-consuming, and doing it from scratch makes the whole endeavour even more challenging. JavaScript remains to be one of the most widely used programming languages geared to serve both the server and client sides.

Its rich ecosystem offers a vast number of open-source frameworks and libraries facilitating the coding acceleration and helping to avoid unnecessary bugs.

Free JavaScript tools often have to be complemented with paid UI libraries for tackling specific development tasks with the utmost efficiency. Generally, the use of ready-made UI components in the development flow provides the following advantages:

• Reducing time and monetary development costs
• Making the final product more affordable for end-users (replacing expensive tools like MS Project)
• Ensuring software consistency (unified design and API)

But seeing the abundance of available JavaScript tools, web developers frequently find it challenging to choose the right ones for their projects. In this article, we can try to help coders with this tricky issue by highlighting the most popular open-source frameworks and commercial JavaScript libraries.

Open-Source JavaScript Frameworks

According to the State of Frontend 2020 survey, web developers still prefer choosing React, Vue.js, and Angular from the realm of JavaScript frameworks as their development helpers. Therefore, we’ve decided to include these well-known technologies in our review and complement this trio with the most popular CSS framework – Bootstrap.

1. React

Created and supported by the Facebook development team, React is the most requested front-end framework dedicated to building versatile and SEO-friendly web UIs. This component-based framework supports unidirectional dataflow, providing enhanced control over the whole project. It makes use of the Virtual DOM to boost the UI rendering.

It is helpful for apps requiring regular updates such as Instagram. Migrating between React versions is very simple, as the process is automated in many ways with “code mods” provided by Facebook.

2. Angular

Angular JS is the most powerful and mature but at the same time, complex JS frameworks. Backed by Google resources, Angular serves for building large-scale and feature-rich enterprise SPAs using HTML and TypeScript. It has a component-based architecture that increases the quality of written code and introduces two-way data binding to display info to end-users.

Utilizing a developer-friendly Angular CLI and intuitive template syntax helps to make the whole development process faster and more productive. Angular ensures the enhanced performance of apps due to a new rendering engine named Ivy added this year in v9.0.

3. Vue.js

Vue.js is the fastest-growing JavaScript framework utilized by developers to create more maintainable and testable code bases for UIs and SPAs. This lightweight (just 20KB) tool benefits from adopting the strengths of the competitive technologies, namely two-way data binding (Angular) and virtual DOM (React).

When programming with Vue.js, it is possible to use JS and TypeScript equally well and take advantage of reusable components. Framework’s pre-coding structure allows developers to promptly deploy the app without any negative impact on its performance. Thanks to its approachable nature, web developers won’t have to spend much time properly learning Vue.js.

4. Bootstrap

Bootstrap is probably the first thing that comes to mind when referring to styling and designing responsive mobile and web apps. In simple terms, Boostrap is a mix of CSS, HTML, and JS code. This CSS framework comes with a vast array of design templates for common UI elements (buttons, forms, dropdowns, etc.) and other pre-built components that can be implemented in web projects and fully customized to match any design requirements.

Also, Boostrap provides useful themes, examples, and JS plugins to help web developers in making their apps more visually compelling. There are also special Bootstrap libraries enabling developers to incorporate UI elements in applications based on Angular, React, or Vue without additional coding efforts.

Commercial JavaScript Libraries

Suppose you need ready-made tools for quickly adding complex functionalities such as Gantt charts, Schedulers, or diagrams with editing and customization capabilities to your project. In that case, it is better to consider using commercial JavaScript libraries.

Web developers who invest in commercial JS components receive a rich feature set out of the box, regular updates as well as bug fixes and emergency bug fixes, and timely technical support. Now let us review several paid JavaScript libraries and how they can help in web projects.

5. DHTMLX

DHTMLX provides a JavaScript UI framework comprising plenty of powerful and fully customizable UI components such as Gantt chart, Scheduler, Kanban, Diagrams, Spreadsheet, and many others. In terms of functionality, DHTMLX Gantt and Scheduler can be a great alternative to MS Project, Primavera, and Excel in project management apps.

All DHTMLX UI widgets are notable for modern Material design, advanced performance with a large amount of data, extensive and straightforward API that can be quickly mastered and put to use. DHTMLX is compatible with React, Angular, and Vue.js, and supports TypeScript.

6. Kendo UI

Kendo UI is an HTML5 user interface framework intended for creating enterprise-level applications running well in various production environments. It contains a large package (70+) of out-of-the-box UI widgets with multi-framework support that look native on any device.

The popular MVVM feature of this UI framework ensures robust two-way data binding capabilities. Using a variety of built-in customizable themes, it is possible to adjust the look and feel of Kendo-based apps to your liking.

7. Syncfusion Essential JS 2

Syncfusion Essential JS 2 is a JavaScript toolkit of diverse controls (grids, charts, calendars, etc.) helping to minimize the number of third-party components required to complete an enterprise application.

Entirely written in TypeScript, all these components are modular and can be independently incorporated into any project regardless of your JS framework choice.

Thanks to a responsive design and support for touch devices, apps developed with the Syncfusion package are rendered nicely across different platforms, thereby reaching more end-users.

8. Sencha Ext JS

Ext JS is Sencha’s JavaScript framework that provides a rich set of configurable ready-to-use components for building enterprise-grade web applications. There are also many additional user extensions supplied by the Sencha community.

When coding with Ext JS, it is possible to make use of different scripting techniques, MVC/MVVM architectural patterns, and OOP concepts. The framework is complemented with a variety of special tools enhancing your capabilities without extra manipulations in the main areas of the programming process – design, development, and testing.

Summarizing the above, we can say that all reviewed JavaScript frameworks and libraries have something useful to offer to any web development project and it’s up to development teams to choose the right option depending on their needs.

The commercial JS components are compatible with most of the open-source frameworks allowing web developers to put them together in powerful toolkits for completing resource-demanding enterprise apps.

For professionals from the field of data analytics, Python for data science is a must-learning skill. As the IT industry rises, demand for professional data scientists is booming, and Python has emerged as the most common data-driven programming language.

Why Learn Python?

Python is one of the essential data science languages if not one. It’s effortless to understand, free, many businesses use it, and there is a range of powerful libraries for statistical visualization and data viewing. One phrase: sooner or later, you have to learn Python if you are looking for a data science career. See the steps below.

1. Set Your Machine

It’s time to start your machine now that you’ve set up your mind. Download Anaconda from Continuum.io, which is the best way to proceed. It comes with most stuff that you’re going to need ever. The main drawback is that, even if the underlying library updates are available, you will need to wait for the Continuum to upgrade its bundle. If you’re a novice, it should hardly matter.

2.  Learn Python Basics

Begin with the basics of the language, libraries, and data structure. Free course in Python from reputed portals is one of the best locations to launch your career learning path. This course aims to get started with Python for data science and make the basic concepts of the language comfortable.

3. Plan for Some Mini Python Projects

You can try to program stuff like online game calculators or a program to gather weather from Google in your area. It’s also possible to develop simple games and applications to help you get to know Python.

Building such mini-projects can help you to learn Python. Projects such as these are joint in all languages and a perfect way to improve your basic knowledge. You should start developing your API experience and start web scraping. In addition to helping you learn Python programming, web scraping is helpful later to collect data.

Boost your learning and find answers to your problems with Python programming. To learn best practices in Python and data science – and to acquire new ideas, read tutorials, blog posts, and even open-source code for other people.

4. Know Python Data Science Libraries

It is where Python’s real strength with data science is introduced. Python has various science programming databases, analyses, and displays. There are some of them in the following list:

Numpy: It is an integral data science library of Python, meaning ‘Numerical Python.’ It’s used for scientific computation with an efficient n-dimensional array object and provides C, C++ integration tools. It can also be used for generic data as a multidimensional container where many Numpy operations and special functions can be performed.

Pandas: It is a primary Python Data Science library. It is used to manipulate and analyze data. It is suitable for various data, including tabular, ordered, and unordered time series and matrix data.

MatplotLib: It is a versatile Python visualization library. Matplotlib is used in scripts, shells, web services, and other GUI toolkits. Using Matplotlib, you can use several plots and how several schemes work.

Seaborn: It is a Python statistical plotting library. If you use Python for Data Science, you will be drawing statistical graphics by MatplotLib (in 2D viewing) and Seaborn, which have their beautiful standard styles and high-level GUI.

Scikit-Learn: One of the key attractions is Scikit learning, where you can use Python to introduce machine learning. It is a free library with easy and powerful tools for analyzing and mining data. You may use Scikit-Learn to implement different algorithms like logistic regression and time series algorithms.

5. Develop a Portfolio of Data Science

A portfolio is necessary for aspiring data scientists. These initiatives should involve interacting with many different datasets so that readers can glean exciting insights. Some types of industries to take into account:

Data Cleaning Project — If you clean up and evaluate dirty or ‘unstructured’ data from any projects, potential employers will get impressed as most real-world data need cleaning.

Data Visualization Project — Making visualizations appealing and understandable is both a programming and a design challenge, but the research will significantly affect if you can do it correctly. You will stand out with fantastic charts in a project.

Machine Learning Project — You certainly need a project to demonstrate your ML chops if you like to work as a data scientist role (and you may want to have a few different machine learning projects, each with an additional standard algorithm because of your work).

6. Apply Advanced Data Science Techniques

Lastly, you have to develop your skills. Your career path into data science will be full of learning, but specialized courses are available, ensuring all the basics are covered. You might be comfortable by knowing regression, classification, and k-means clustering models. You can also join machine learning – bootstrapping models and the creation of neural networks using Scikit-Learn.

Projects may include the creation of models using live data feeds at this stage. This type of machine learning model adjusts its predictions over time.

Start Your Interactive Python Course Right Now!

If you are starting your Python journey, the most you can do is lay yourself on the basics. Our introductory Python courses are interactive and allow you to apply as you learn. There are plenty of Python learning options available. Still, it is better to pick somewhere that teaches data science explicitly if you are looking to learn a Python Data Science course.

Python is also used in various other programming disciplines ranging from game development to smartphone applications. You need not require previous programming experience, and as soon as you finish your project, you will have developed from the ground up a real data science project using your new Python language skills!

Learn Python for Data Science with the help of an abundance of online courses, tutorials, and workshops and start working with oceans of data faster rather than later. There are practically infinite professional opportunities from there.