How will data science platforms evolve with the help of APIs?

The industries that use data science are beginning to innovate more and more. With the help of APIs, there’s no limit on how far data science can evolve. APIs give businesses access to understandable data that can be used to fuel innovation and growth. They help open up new markets, inspire new business models, and enable business partnerships to form. 

Because APIs help business users gather actionable insights to make more informed business decisions, industries can continue to create and break new ground, as we’ve seen with the innovations in healthcare, finance, cybersecurity, and the automotive industry. 

With more businesses becoming data-driven with the help of APIs, the future of data science is unlimited.

What is data science?

Data science applies advanced analytic techniques and scientific principles to data to form actionable insights and inform better decision-making. The insights gathered from data science can help businesses increase operational efficiency, identify new opportunities, and improve systems. Data science is a way to apply what is learned to real-world applications.

Data science also incorporates various disciplines, including data engineering, data preparation, data mining, predictive analytics, machine learning, and data visualization.

What is a data science platform?

A data science platform is software that includes various technologies for machine learning and other advanced analytics uses. A data science platform allows data scientists to discover actionable insights, communicate those insights, and plan strategically and proactively.

Data science vs. business intelligence

Think of data science in terms of the future and business intelligence (BI) in the present. Data scientists deal with more specific predictive and prescriptive analytics and look at what will happen; business users deal with more generalized and descriptive analytics to see what has already happened. 

ETL Tools for Business Intelligence

For BI to work, data needs to be integrated with the support of ETL (extract, transform, and load) tools. ETL tools load data into one centralized location and turn raw data into actionable information that businesses can use to make more informed and strategic decisions.

ELT Software for Data Science

ELT software can be seen as the future of ETL as it streamlines the data loading process. It is a data integration process where data is extracted from one or multiple sources and loaded into a target data system. And instead of transforming the data before it’s loaded as with ETL tools, ELT software uses the target system to transform the data. 

Why is a data science platform important?

A data science platform puts the data modeling process into the hands of data scientists. With this immediate access to data, data science teams can gather actionable data insights and communicate them to business stakeholders to inform better decision-making. As a result, data science can provide customer insights, help with financial risk management, block cyberattacks, optimize supply chain management, and streamline customer service. 

What can you do with a data science platform?

• Explore data on large machines 
• Use new packages and tools
• Find, understand, and build on past work
• Put security boundaries around work
• Track and share your work
• Publish models as APIs
• View work in the form of self-service tools

How does a data science platform work?

Data science platforms support data scientists by helping them run, track, reproduce, share, and deploy models fast and efficiently. A data science platform helps on an organizational level by connecting people, tools, and products across the data science lifecycle. 

What takes data science a step further is the additional help of APIs. APIs serve as the essential building blocks for data science, providing key data sources that enable data integration and visualization. In other words, APIs help make complex data and advanced analytics easier to understand so business users can gain the actionable insights they need to make informed business decisions.

APIs and Data Science Today

APIs for data science are useful as they’re essential building blocks. They are pieces of code that can be put together to enhance applications and websites. For example, speech recognition APIs like chatbots improve the connection between models and consumers. And machine learning APIs bring together predictive analytics so businesses can better understand their customers.

APIs and the Future of Data Science

Data science is consistently evolving, and it has the potential to grow beyond its current capabilities with the additional help of APIs. APIs enable different industries to innovate, improve, and become more data-driven. Additionally, they pave the path for new business partners and help with app development. APIs have the potential to make advanced analytics more understandable so that better business forecasting can take place.