Low-code platforms mean anyone can be a developer and maybe a data scientist, too

Low-code is a software development approach that leverages a visual user interface to create applications instead of traditional hand-coding. Data science is an evolving profession. Artificial intelligence is also changing at a remarkable pace. Several new platforms and tools are being regularly rolled out to help data scientists do their jobs more effectively and efficiently. Low-code platforms are democratizing application development and allowing all kinds of business professionals to create software solutions to the challenges they face.

Here is how Low-code platforms ensure anyone can be a data scientist:

Quicker Time to Market

Low-code platforms can speed development by offering reusable components needed throughout the Artificial Intelligence lifecycle — data connectors, data handlers, backend/frontend development modules, ML algorithms, visualization widgets, and administration and security modules.

Resolving Talent shortage

Data science practitioners who can solve business challenges by applying AI are in short supply. In many cases, there is a significant time lag between a request and when that request is fulfilled. This takes up a lot of the team's time and creates frustration among various stakeholders. Given organizations' growing number of data requests, data scientists can't contribute to this bottleneck. By offering an intuitive drag-and-drop interface, low-code platforms crash the barriers to data science development.

Need for Cost Savings

Every IT department faces the same problem of diminishing budgets and increasing pressures to prove ROI for every initiative. Enterprises are increasingly turning to low-code/no-code platforms as an agile method of developing new applications that let them turn new ideas into working solutions, and then scale up to the production environment when ready.