What is a data scientist?
As a specialty, data science is young. It grew out of the fields of statistical analysis and data mining. The Data Science Journal debuted in 2002, published by the International Council for Science: Committee on Data for Science and Technology. By 2008 the title of data scientist had emerged, and the field quickly took off.
Who oversees the data science process?
At most organizations, data science projects are typically overseen by three types of managers:
Business managers: These managers work with the data science team to define the problem and develop a strategy for analysis. They may be the head of a line of business, such as marketing, finance, or sales, and have a data science team reporting to them. They work closely with the data science and IT managers to ensure that projects are delivered.
IT managers: Senior IT managers are responsible for the infrastructure and architecture that will support data science operations. They are continually monitoring operations and resource usage to ensure that data science teams operate efficiently and securely. They may also be responsible for building and updating IT environments for data science teams.
Data science managers: These managers oversee the data science team and their day-to-day work. They are team builders who can balance team development with project planning and monitoring.
But the most important player in this process is the data scientist.
The benefits of a data science platform
A data science platform reduces redundancy and drives innovation by enabling teams to share code, results, and reports. It removes bottlenecks in the flow of work by simplifying management and incorporating best practices.
In general, the best data science platforms aim to:
- Make data scientists more productive by helping them accelerate and deliver models faster, and with less error
- Make it easier for data scientists to work with large volumes and varieties of data
- Deliver trusted, enterprise-grade artificial intelligence that’s bias-free, auditable, and reproducible
Data science platforms are built for collaboration by a range of users including expert data scientists, citizen data scientists, data engineers, and machine learning engineers or specialists. For example, a data science platform might allow data scientists to deploy models as APIs, making it easy to integrate them into different applications. Data scientists can access tools, data, and infrastructure without having to wait for IT.
The demand for data science platforms has exploded in the market. In fact, the platform market is expected to grow at a compounded annual rate of more than 39 percent over the next few years and is projected to reach US$385 billion by 2025.