Data science applications provide insights into large and complex data sets, oftentimes including powerful models that have been carefully crafted to fulfill customer needs. However, the insights generated are not useful unless they are presented to the end-users in an accessible and understandable way. This is where building a web app with a well-designed frontend comes into the picture: it helps to visualize customizable insights and provides a powerful interface that users can leverage to make informed decisions effectively.
In this article, we will discuss why a frontend is useful in the context of data science applications and which steps it takes to get there! We will also give a brief overview of popular frontend and backend frameworks and when these setups should be used.
Three reasons why a frontend is useful for data science
In recent years, the field of data science has witnessed a rapid expansion in the range and complexity of available data. While Data Scientists excel in extracting meaningful insights from raw data, effectively communicating these findings to stakeholders remains a unique challenge. This is where a frontend comes into play. A frontend, in the context of data science, refers to the graphical interface that allows users to interact with and visualize data-driven insights. We will explore three key reasons why incorporating a frontend into the data science workflow is essential for successful analysis and communication.
Visualize data insights
A frontend helps to present the insights generated by data science applications in an accessible and understandable way. By visualizing data insights with charts, graphs, and other visual aids, users can better understand patterns and trends in the data.
Depiction of two datasets (A and B) that share summary statistics even though the distribution differs. While the tabular view provides detailed information, the visual presentation makes the general association between observations easily accessible.
Customize user experiences
Dashboards and reports can be highly customized to meet the specific needs of different user groups. A well-designed frontend allows users to interact with the data in a way that is most relevant to their requirements, enabling them to gain insights more quickly and effectively.
Enable informed decision-making
By presenting results from Machine Learning models and outcomes of explainable AI methods via an easy-to-understand frontend, users receive a clear and understandable representation of data insights, facilitating informed decisions. This is especially important in industries like financial trading or smart cities, where real-time insights can drive optimization and competitive advantage.
Four stages from ideation to a first prototype
When dealing with data science models and results, the frontend is the part of the application with which users will interact. Therefore, it should be clear that building a useful and productive frontend will take time and effort. Before the actual development, it is crucial to define the purpose and goals of the application. To identify and prioritize these requirements, multiple iterations of brainstorming and feedback sessions are needed. During these sessions, the frontend will travers from a simple sketch over a wireframe and mockup to the first prototype.
Sketch
The first stage involves creating a rough sketch of the frontend. It includes identifying the different components and how they might look. To create a sketch, it is helpful to have a planning session, where the functional requirements and visual ideas are clarified and shared. During this session, a first sketch is created with simple tools like an online whiteboard (e.g., Miro) or even pen and paper can be sufficient.
Wireframe
When the sketching is done, the individual parts of the application need to be connected to understand their interactions and how they will work together. This is an important stage as potential problems can be identified before the development process starts. Wireframes showcase the usage from a user’s point of view and incorporate the application’s requirements. They can also be created on a Miro board or with tools like Figma.
Mockup
After the sketching and wireframe stage, the next step is to create a mockup of the frontend. This involves creating a visually appealing design that is easy to use and understand. With tools like Figma, mockups can be quickly created, and they also provide an interactive demo that can showcase the interaction within the frontend. At this stage, it is important to ensure that the design is consistent with the company’s brand and style guidelines, because first impressions tend to stick.
Prototype
Once the mockup is complete, it is time to build a working prototype of the frontend and connect it to the backend infrastructure. To ensure scalability later, the used framework and the given infrastructure need to be evaluated. This decision will impact the tools used for this stage and will be discussed in the following sections.
There are many Options for Frontend Development
Most data scientists are familiar with R or Python. Therefore, the first go-to solutions to develop frontend applications like dashboards are often R Shiny, Dash, or streamlit. These tools have the advantage that data preparation and model calculation steps can be implemented within the same framework as the dashboard. Visualizations are closely linked to the used data and models and changes can often be integrated by the same developer. For some projects this might be enough, but as soon as a certain threshold of scalability is reached it becomes beneficial to separate backend model calculation and frontend user interactions.
Though it is possible to implement this kind of separation in R or Python frameworks, under the hood, these libraries translate their output into files that the browser can process, like HTML, CSS or JavaScript. By using JavaScript directly with the relevant libraries, the developers gain more flexibility and adjustability. Some good examples that offer a wide range of visualizations are D3.js, Sigma.js or Plotly.js with which richer user interface with modern and visually appealing designs can be created.
That being said, the range of JavaScript based frameworks is still vast and growing. The most popular used ones are React, Angular, Vue and Svelte. Comparing them in performance, community and learning curve shows some differences, that in the end depend on the specific use cases and preferences (more details can be found here or here).
Being “the programming language of the Web”, JavaScript has been around for a long time. It’s diverse and versatile ecosystem with the advantages mentioned above is proof of that. Not only do the directly used libraries play a part in this, but also the wide and powerful range of developer tools that exist, which make the lives of developers easier.
Considerations for the Backend Architecture
Next to the ideation the questions about the development framework and infrastructure need to be answered. Combining the visualizations (frontend) with the data logic (backend) in one application has both pros and cons.
One approach is to use highly opinionated technologies such as R Shiny or Python’s Dash, where both frontend and backend are developed together in the same application. This has the advantage of making it easier to integrate data analysis and visualization into a web application. It helps users to interact with data directly through a web browser and view visualizations in real-time. Especially R Shiny offers a wide range of packages for data science and visualization that can be easily integrated into a web application, making it a popular choice for developers working in data science.
On the other hand, separating the frontend and backend using different frameworks like Node.js and Python provides more flexibility and control over the application development process. Frontend frameworks like Vue and React offer a wide range of features and libraries for building intuitive user interfaces and interactions, while backend frameworks like Express.js, Flask and Django provide robust tools for building stable and scalable server-side code. This approach allows developers to choose the best tools for each aspect of the application, which can result in better performance, easier maintenance, and more customizability. However, it can also add complexity to the development process and requires more coordination between frontend and backend developers.
Hosting a JavaScript-based frontend offers several advantages over hosting an R Shiny or Python Dash application. JavaScript frameworks like React, Angular, or Vue.js provide high-performance rendering and scalability, allowing for complex UI components and large-scale applications. These frameworks also offer more flexibility and customization options for creating custom UI elements and implementing complex user interactions. Furthermore, JavaScript-based frontends are cross-platform compatible, running on web browsers, desktop applications, and mobile apps, making them more versatile. Lastly, JavaScript is the language of the web, enabling easier integration with existing technology stacks. Ultimately, the choice of technology depends on the specific use case, development team expertise, and application requirements.
Conclusion
Building a frontend for a data science application is crucial in effectively communicating insights to the end-users. It helps to present the data in an easily digestible manner and enables the users to make informed decisions. To ensure that the needs and requirements are utilized correctly and efficiently, the right framework and infrastructure must be evaluated. We suggest that solutions in R or Python are a good starting point, but applications in JavaScript might scale better in the long run.
If you are looking to build a frontend for your data science application, feel free to contact our team of experts who can guide you through the process and provide the necessary support to bring your visions to life.