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Java Script for Data Science: Libraries, Tools & Use Cases
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26th Apr, 2024
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JavaScript is an object-oriented programming language. We all know that JavaScript is important for developing graphics and visualizations on the internet. Whether viewing a website or interacting with an application, you may be manipulating JavaScript code in some way without even knowing it. This is because Javascript is primarily used to build websites and applications. JavaScript is often combined with other web development and design-oriented languages like HTML and CSS to create a dynamic and engaging user experience. Whether you are coding for front-end or back-end development, JavaScript has many uses when it comes to ramming. But here, the question is, can JavaScript be used in Data Science?
In 2018, Tensorflow introduced JavaScript interfaces to its list of supported languages. This came as a huge surprise to many in the Data Science community, but the decision makes sense. JavaScript is a very popular programming language. Anyone who is developing JavaScript will want to take advantage of it. In Data Science, it is more important to stay up-to-date with technology. It may not seem like it, but JavaScript is no exception to this rule. So, in this article, let’s discuss how we can implement data science in JavaScript.
Can You Use JavaScript for Data Science?
JavaScript is known for its web development features, but how do these features translate to data science applications? First, remember that JavaScript is not a statistical programming language. The language was built for web development, and that is what makes it stand out. From a typical statistician’s perspective, data science in JavaScript can be a little difficult to understand. However, this used to be true for Python as well. Python itself was never destined to lead t, it established itself as a great language through its general prominence.
JavaScript is also no exception for this case. With the help of new developing tools and libraries, JavaScript also can be used in Data Science.
When Does a Data Scientist Use JavaScript?
Machine learning capabilities in JavaScript are evolving rapidly. It may already be in use as a powerful alternative to regular data science languages for some use cases. Data scientists use JavaScript for visualizations, asynchronous tasks, and real-time processing of data. They can use technologies like D3.js to create beautiful and interactive charts that let users explore complex data. They can use NodeJS and SocketIO to process real-time data. JavaScript plays an important role throughout the data science workflow. Check out the Data Science Certificate Online course for more knowledge about data science with JavaScript.
Uses of JavaScript in Data Science
Data scientists can use JavaScript in the same way that developers can use the language. But the JavaScript language has some uses regarding manipulating information and data and developing data science projects. JavaScript is used in data science for developing and designing websites, databases, and applications, for data visualization and charting, and for new ways to build machine learning models. Let’s discuss these uses in further points with detail.
1. Data Visualizations and Interactive Models
JavaScript is mainly known for being used to create the phenomena of an application or a website. One of the important reasons data scientists should consider this programming language is for creating data visualizations. We can use JavaScript to create interactive models and share them with the audience in more engaging and visually interesting ways. For example, instead of creating static or 2D charts and graphs, JavaScript allows us to use our skills to make videos and graphs of data findings rather than reconstructing them from the application or software. JavaScript provides the ability to create 3D models.
There are several libraries available for creating data visualizations and models using the JavaScript programming language. These libraries also contain a variety of interesting and innovative models.
2. Website and Application Development
The simplest way data scientists can use JavaScript is in the process of web development and web design. In the data science industry, there are several tools being developed to transform data analysis, visualization, and reports into applications that can be shared with anyone. It is important for data scientists to have knowledge of the process of platform development. Because data scientists are working in an industry that is focused on combining data and technology, such as social media and Fintech, this knowledge is useful when working in teams or developing our own products.
Additionally, data scientists can use JavaScript to update professional portfolios with visualizations and models created in the language. This will allow them to put their JavaScript skills to good use through various aspects of their online presence.
3. Automation and Machine Learning Models
JavaScript is also used for automation and machine learning, besides graphics and other forms of visualizations. This is because the JavaScript programming language was evolving. One of its many software libraries that developers frequently use is TensorFlow. TensorFlow is an open-source library that enables everyone using the JavaScript language to run their own machine-learning models. The TensorFlow library is useful for building machine learning models in web browsers, especially when working with artificial neural network algorithms and training different models. This library is also useful for platform and product testing processes.
4. How to Use JavaScript in Data Science
JavaScript has many unique properties that make it suitable for data science. A non-blocking, I/O structure ensures efficient memory and CPU allocation for high-throughput processing tasks. But what is exciting is the new JavaScript tools at every level of the data science stack.
JavaScript Tools in Different Data Science Layers
Let’s go into more detail of what are the levels of the data science stack and which JavaScript tools are used in each level.
1. The IDE (Integrated Development Environment)
Every good data scientist needs a development environment that helps them explore data sets, test code and build algorithms. Everyone has their favorite environment, but I would like to recommend Jupyter. It started as a project to create a browser-based collaborative environment to enable scientific computing in Python. Later that project was renamed Jupyter after extending its functionalities to other languages.
Jupiter's clean interface and ability to combine code with HTML and Markdown make it a great option for developing and sharing notebooks. Jupyter can support JavaScript and NodeJS. Whether you are new to data science or using another language from Jupyter, it is easy to switch to JavaScript/Node in the IDE.
2. The libraries
IDEs are great, but nothing beats the algorithms you can build in an IDE. The ability to efficiently build meaningful algorithms is key to any good data science stack, and it is highly dependent on the available libraries and the community behind them. The balance of powerful yet usable libraries that make statistical and machine learning tasks more accessible is mostly created for Python. The libraries are nltk, scipy, NumPy, pandas, sciki-learn, etc. This is largely because they have a large community on platforms like GitHub.
However, take a closer look at NPM. You will see a growing community for these equivalent packages has been created in Node.js. There are different libraries and packages in JavaScript that can be used for different filters and algorithms.
3. The Processing
Hadoop is the biggest name in big data processing, and it won’t be superseded anytime soon. But older paradigms like MapReduce are being replaced by newer projects like Apache Spark. Spark runs so much faster than MapReduce. The processing time can be significantly faster and more accurate than traditional means. It turns out that Spark doesn’t support JavaScript!
However, because of the above-mentioned growing data community and the impressive Node-Spark project by Henry DF, this is not such a big problem. This Node-Spark project creates bindings around Spark’s DataFrame API so you can continue to use Spark functionality without switching context to another programming language.
4. The Processing Alternative
Spark is a very impressive tool, but apart from the wrapper, it is not a native JavaScript framework. There is another alternative to this, and it is Skale. Skale is a next-generation, distributed processing platform. Programmers can interact with the API via native JavaScript. And unlike Node-Spark, the backend runs entirely on Node.
5. The Pipelines
Let’s take a quick recap. You have done your analysis in the Node IDE, built algorithms using Node libraries, and used Node wrappers for data processing for your algorithms. Then this leads you to a dashboard or an app (probably written in JavaScript). Here comes Aries. Aries is used to create pipelines between all these JavaScript-centric stages. Aries is the only workflow (pipeline) builder that requires only JavaScript knowledge.
Aries is written in such a way that each pipeline is divided into modular composite stages (activities), which can use any combination of sources, targets, and transformations. These integrations are written to help web developers to become data engineers.
You can now build a complete data science stack without leaving JavaScript from the development environment to the processing engine using the data science stack and tools mentioned above. For more understanding of these layers, you can check out the Bootcamp for Data Science. You can enroll in the JavaScript data science course for more concepts.
Top 7 JavaScript Libraries for Data Science
When it comes to JavaScript development, the NPM registry has a lot of cool open-source projects and packages that you might find useful in your projects.
Here let’s review 7 JavaScript data science libraries or NPM packages that are most useful for developers to manipulate data, visualize and perform machine learning.
1. D3.js
D3, or Data-Driven Documents, is one of the JavaScript data science libraries that lets you manipulate data and get custom data visualizations using HTML, CSS, and SVG. D3 can combine documents with the document object model and transform documents based on requirements. D3 includes a wide variety of graph types for data analysis, such as boxplots, histograms, hierarchies such as treemaps, networks such as chard graphs, as well as common graphs such as scatterplots, line graphs, bar graphs, and pie charts. There are also animated treemaps, zoomable bar charts, bar chart races, and more.
2. Sigma.js
Graphs are a particularly important part of data visualization, and sigma.js has a special focus on drawing graphs. It has built-in functionality to easily visualize charts and publish them to web pages. In addition to Canvas and WebGL support, Sigma.js offers options such as mouse and touch support, custom rendering, and additional accessibility. You can change data, move cameras, listen to events, and change rendering at will. An interactive layer to add to your chart.
3. Chart.js
Chart.js is an open-source JavaScript charting library that provides eight comprehensive chart types, including all common charts such as bar charts, pie charts, histograms, scatter charts, error charts, and more. All these charts can be combined to create customizable and animatable mixed charts. Chart.js also renders smoothly in all web browsers and resizes charts according to the web browser window size. All graphs in this library can also be combined with the Moment.js library if you need a timeline.
4. Tidy.js
Tidy.js is a data-wrangling library built from the basics of JavaScript. This is the easiest place to start when it comes to manipulating data in JavaScript apps. This library is easiest to learn if you have some knowledge of JavaScript arrays and objects.
5. Apache Arrow
This project aims to develop Arrow technology to make deserializing data from large clusters of data formats into in-memory solutions such as the Pandas library in Python. This is easier and faster than existing solutions.
6. Cytoscape.js
Cytoscape is a comprehensive JavaScript library for chart analysis. This is a little-known library that allows developers to visualize their data in a unique way in the browser. It includes all the gestures you'd expect in a visualization library, such as pinch to zoom, box select, and pan. Supports both web and mobile applications.
7. Three.js
Three.js is a 3D visualization library. Have you ever wondered how Github created this creative globe for its landing page? Yes, they enlisted the help of three.js to make their job easier. If you want to bring your 3D models to life on the web, this library is a must.
8. TensorFlow.js
TensorFlow is a well-known library to almost everyone in the software industry. Tensorflow is now also available in JavaScript. It allows developers to build machine learning models directly in the browser or on the backend via Node.js.
Real-world Use Cases of JavaScript in Data Science Workflow
We can see many examples of use cases of data science using JavaScript and its workflow. Here, let’s discuss the real-world example of the use of JavaScript in data science.
Let’s take the medical field as an example. We can develop a wearable device, and for that, we will need to collect user activity data. JavaScript runs on nearly every platform, including wearables. Using JavaScript, we can reduce compatibility issues and allow data scientists to collect data from medical devices and run algorithms in an optimized manner. Besides this, there are other real-world applications of data science in the medical field, like medical image analysis, predictive analysis, drug research, genomics, etc.
Pros and Cons of Using Java Script for Data Science
If you have been following technology trends over the last few years, you have probably noticed that JavaScript is a very popular language these days. It has grown in popularity since Node.js enabled JavaScript developers to write server-side code. You've also noticed that there's a lot of excitement in the field of data science, especially machine learning. Recent advances in theory and technology have made this once-challenging area more accessible to developers.
So here the question is, should Data Scientists Consider Learning JavaScript? Is JavaScript useful for data science? Sometimes using JavaScript vs. Python for data science is also a question.
Most data scientists work with a combination of Python, R, and SQL. A data scientist can also specialize in another language, such as Scala or Java.
But some data scientists choose projects in data science using JavaScript. But given the importance of JavaScript and the popularity of data science, how much can a data scientist benefit from learning the basics of the language? Let’s discuss some key pros and cons of JavaScript in data science.
| Pros | Cons |
|---|---|
Visualization: JavaScript is great for data visualization. Libraries such as D3.js, Chart.js, and Plotly.js make it incredibly easy to create powerful data visualizations and dashboards. | Functionality: JavaScript lacks a wide range of data science packages and built-in functions compared to languages like R and Python. If you don't mind reinventing the wheel, this might not be too much of a problem. But when you need to do more advanced analysis, you quickly run out of options. |
Product Integration: More and more companies are using web technologies with node-based stacks to build their core products or services. As a data scientist, if you need to work closely with product developers, "speaking" the same language is fine. | Productivity: Another benefit of the extensive Python and R ecosystem is the abundance of guides and tutorials for almost any data science task you want to do. This doesn't really apply to JavaScript. It will probably take longer than Python or R to understand how to solve data science problems in JavaScript. |
ETL: Data processing pipelines are typically implemented in general-purpose languages such as Python, Scala, and Java. JavaScript is often invisible. However, this may be unfair. Node's fs filesystem module provides a nice API that allows standard filesystem operations to be called synchronously or asynchronously. Node also works well with MongoDB and many other popular database systems. | Multithreading: It is often useful to process large datasets and run simulations in parallel. However, Node.js is not suitable for computationally intensive, CPU-intensive tasks. Languages such as Python and Java outperform JS for such tasks. But take a look at Microsoft's Napa.js project. It provides a multi-threaded JavaScript runtime that can complement Node.js. |
TensorFlow.js: JavaScript can also do machine learning. In 2018, TensorFlow was released. It brings machine learning to JavaScript developers, both on the browser and server-side. Tensorflow is a popular machine-learning library developed by Google and released as open-source in 2015. It performs functions like gesture recognition, object recognition, composition, etc. | Opportunity Cost: Opportunity cost is probably the main reason why data scientists tend not to learn many languages other than Python and R. An hour spent learning another language could have been spent learning a new Python framework or another R library. These languages dominate the data science job market, but there are great incentives to learn them. Also, data science is a rapidly changing field, so there is always something new to learn. |
Conclusion
Now, if we must summarize the above article, we can say that JavaScript is a versatile language. It has many uses in web development, data science, and machine learning. Data science in JavaScript is only possible because of its growing community and its never-ending research around the programming language. There are some limitations to the usage of the language, but we can use alternative options for them, and developing projects and packages is the solution for them. For more deep concepts and knowledge of data science, you can visit KnowledgeHut’s Data Science Certificate Online.
Frequently Asked Questions (FAQs)
1. Is JavaScript good for machine learning?
Python or R are the most used languages for machine learning applications. But JavaScript has some advantages over them, which is why Javascript has a good future in machine learning. Some of the advantages are JavaScript is well-known and it is faster than Python.
2. Is JavaScript faster than R?
Every time R sees a variable it has to check its type, which adds a lot of processing overhead. This makes it easier to change types and reuse variable names but slows down computations for highly repetitive tasks, such as performing actions in loops. But this is not the case in JavaScript. This is the reason that JavaScript is faster than R.
3. What are some JavaScript data science projects?
There are some projects like Human Action Detection, Medical Data Interpretation, Climate Change Impact Prediction, Forest Fire Detection, etc., that can be done in data science. JavaScript can be used in those projects.
4. What are the top four programming languages used by Data Scientists?
The top programming languages for data science are Python, R, Java, Scala, and JavaScript. These are general-purpose programming languages that are used to develop any software and are popular in the data science community.
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