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Emergence of Low/No-code solutions in Data Science

In the field of data science, the prominence of low-code/no-code solutions has grown rapidly. These tools have democratized data analysis and model development, enabling business analysts, domain experts, and citizen data scientists to actively participate in the data science process.

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The idea of creating software and performing complex data tasks without needing to code isn’t new, but it has gained substantial momentum over the past decade, with the emergence and evolution of low/no-code tools.

The concept gained more ground with the proliferation of the internet and cloud computing in the 2000s. Tools like Wix and WordPress democratized web development, allowing users to create websites without needing to code.

In the field of data science, the prominence of low-code/no-code solutions has grown rapidly. These tools have democratized data analysis and model development, enabling business analysts, domain experts, and citizen data scientists to actively participate in the data science process. The intuitive interfaces and automated workflows provided by low/no-code platforms simplify complex data tasks such as data preparation, feature engineering, model training, and visualization, making them more accessible to a wider range of users.

Low-code No-code platforms provide intuitive interfaces, pre-built functionalities, and automation capabilities that simplify and streamline data science processes. Users can visually design workflows, perform data preprocessing, build and train machine learning models, and analyze results through drag-and-drop interfaces or flowchart-like diagrams. These tools enable users to focus on the business logic and problem-solving aspects of data science rather than getting caught up in coding intricacies.

Market Size of Low/No-Code Platforms

As organizations endeavor to transform their vast data repositories into actionable insights, low code no code platforms have emerged as vital tools, democratizing data science by empowering a wider range of personnel to partake in data-driven tasks, including but not limited to data integration, data cleaning, exploratory data analysis, visualization, and machine learning.

This is evident in our finding as well, as the global market size of low code no code data science platforms is USD 3.8 billion. Moreover, It is estimated to reach an expected value of USD 13 billion by 2028, growing at a CAGR of 28.6% during the forecast period (2023–2028).

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