The power of data has been proven by many case studies. But to fully leverage it, we need to undergo a complex and time-consuming process of mining, analysis, and visualization. Nowadays, with the help of AI and even low-code, data science can quickly be streamlined and automated.
The Benefits of Using Low-code For Data Science
An EY report reveals that 81% of CEOs view data as key to decision-making. Yet, companies often struggle with managing 80-90% of input data that is unstructured, complicating its use in machine learning (ML) applications.
Developing ML/AI applications that can analyze trends and provide insights is challenging due to its complexity and the lack of qualified technicians with machine learning skills. This demand makes it difficult for business to access the talent needed for tasks like data collection, cleansing, and model training, essential for leveraging AI in digital applications.
Low-code can be the ultimate solution. With its pre-built code blocks, you can quickly create a simple application without touching a line of code.
Yet, unlike simple software builders, low-code tools allow you to customize the front-end and back-end based on your needs. This ensures flexibility and scalability. By saving time on development, you can focus on training your machine learning, cleansing your data model and customizing a data services worker.
Who Can Use Low-code for Data Science
Data engineers can set up compliant data sources, allowing users to tailor data views to their needs. They can switch or add data sources seamlessly, keeping the user experience uninterrupted and enriching the virtual data warehouse with new views.
Coding environments can either be too sophisticated or too simple to suit certain data science’s requirements. Meanwhile, a data scientist typically desires precise control over their learning algorithm.
The low-code environment can help data scientists to:
- Be more flexible with the tools they employ.
- Concentrate on the more demanding aspects of their job.
- Access to new technologies and make their application future-proof.
- Include a mechanism for packaging and deploying trained models.
- A one-stop essential features for data transformations.
Interactions between data science teams and business users are often strained, with unmet expectations on both sides. A low-code platform can facilitate clearer communication by allowing data scientists to visually explain their methodologies, enabling business users to easily understand data flows and offer prompt feedback. This not only accelerates project turnarounds but also fosters a collaborative environment where data and business experts work together more effectively.
Examples of Using Low-code for Data Science
Visual programming has enabled everyone to turn their ideas into reality. Here’re some specific use cases of low-code data science platforms.
Collecting The Data
Data science begins with data collection, which low-code platforms simplify by offering API integrations for automatic data scraping from web sources, eliminating the need for manual collecting.
For startups or small teams, there’s no need to set up servers or databases; low-code platforms provide ready-made connectors for accessing various data sources, both internal and external. These connectors can be easily expanded, enhancing algorithms and supporting real-time, automated data interactions.
Giving Data Structure
Data cleansing is required to give your data a clear structure that can be extracted and analyzed. And this step of purifying and making data readable can easily be attained with low-code, thanks to its automation ability.
For co-founders and business users, the allure of data science lies in the valuable information that hides deep under the data. But lines of numbers and words are much harder to read. While this is not the main feature, low code platforms really excel in visualizing data insights attractively. Their drag-and-drop capabilities further ease data organization, streamlining the creation of functional data flows.
These dashboards and reports can be useful for quarterly evaluations or auditing an organization’s data footprint. Many low-code development platforms have modules for creating visually appealing user interfaces and visualizations based on a dataset.
Top Low-code Data Science Platform
PyCaret streamlines the process for data professionals to rapidly build and deploy machine learning models with its comprehensive features, including data prep, model training, and evaluation. Its popularity as a low-code library is further enhanced by features like model deployment and hyperparameter tuning, all accessible via a unified interface with visualization tools for clearer insight into data and outcomes.
The appeal of PyCaret lies in its ability to reduce the coding required for machine learning projects, freeing up data scientists to concentrate on more critical aspects of their work.
Furthermore, PyCaret’s user-friendly approach lowers the barrier to entry into machine learning, enabling beginners and those from non-traditional backgrounds to explore the field without needing deep knowledge of the algorithms and methodologies involved.
Made by Microsoft, this is currently the leading choice for low-code data visualization, analytics and BI in the market. All of this is thanks to the set of specialized tools and robust coding environment where businesses can translate data into actionable information.
Leveraging AI, specifically through its Copilot capabilities, it can autonomously examine and represent data, freeing you from time-consuming manual work. For those invested in Microsoft’s ecosystem, Power BI offers significant advantages by enabling the integration and centralization of your data across various applications, including Microsoft365 and SharePoint.
If you are looking for support in low-code development, data analytics or even both, Synodus is the certified Gold partner of Microsoft Power BI and Low-code. Having worked with many businesses, from retail to financial, we are confident in our ability to transform your data management with our custom low-code development services and extensive data tools.