Data science platforms are pieces of software that provide access to many different tools for advanced analytics and machine learning. It helps data scientists to develop strategies, discover actionable insights from data, and disseminate these findings throughout an organization in a unified platform.
Data science projects sometimes need the use of many unrelated programs, one for each stage of the modeling process. It is also essential to have a single place where data science teams may cooperate on such initiatives.
Companies are investing in data science systems and sophisticated analytics capabilities to facilitate data-driven business choices. A unified, integrated platform may lead to improved outcomes and, therefore, increased company value.
As a result of the adaptability and interoperability of platforms for data science, businesses may better integrate data-driven choices into both internal and external systems, therefore boosting business results and enhancing the customer experience.
Variations on Data Science Platforms
Platforms for data science are classed as:
- Open Data Science Platform. Data scientists may tailor their workflow to their own needs by using open data science platforms, which allow them to utilize whatever packages they like. A platform for open data science enables data scientists to utilize the appropriate tools for the task at hand and to experiment with alternative languages and technologies.
- Unavailable Data Science Platform. Closed data science platforms require data scientists to use the vendor’s platform-specific programming language, graphical user interface (GUI) tools, and modeling packages. This limits the tools that data scientists may employ on top of the platform.
Importance of Data Science Platform
- Helps delegate numerous low-value tasks.Data science platforms allow data scientists to delegate several low-value activities such as job scheduling, recreating historical findings, generating reports, and customizing environments for non-technical users.
- Assists in minimizing engineering effort.Without needing extra DevOps or technical work, data science platforms allow data scientists to get analytical models into production. If a data scientist at Walmart constructs a product recommendation engine, then a software engineer must test, improve, and merge the data model before the front-end customers may receive product suggestions based on their behavior. A data science platform guarantees that data models are accessible via an API so data scientists may depend less on engineering.
- To improve cooperation among data scientists.The value that a business receives from its data science team plummets if its members, each of whom is an expert in a different area, try to solve the same issue in their unique method. Providing a centralized, adaptable, data science cloud platform with the needed set of tools for data scientists to work on is the best way to assure an effective data science collaboration platform among data scientists. Using a data science platform guarantees that all of the data scientists’ contributions, including data visualizations, data models, and code libraries, exist in a single area that is accessible to everyone. This allows data scientists to promote greater dialogue around research topics, reuse code, and exchange best practices to make data science less resource-intensive, repeatable, and readily scalable.
- Accelerates experimentation and investigation. Data science platforms allow users to see the processes, relieving data scientists of administrative burdens. Moreover, a single platform makes it simpler to maintain the work of the departing individuals than a collection of fragmented tools, making it easier for a recruit to jump right in and begin contributing to the data science team.
The process of data science starts with examining the data to determine what is available for analysis. Without knowing what other people on your team have achieved, you may waste time re-creating the wheel throughout the ideation and exploration stages.
Data scientists conduct experiments to test various hypotheses, analyze the results, and make adjustments. Without a data science platform, this step of the data science process is likely to stutter if the tests being conducted are computationally expensive.
Data science projects must be operationalized for any value to be derived from the results of the analysis. This necessitates engineer resources, generating more expenses and extending the time to market.