The data science platform market is slated for the exponential growth of more than 40% in the next two years. A forecast by market research predicts numbers as high as 66% of growth in the next 3 years. All this gives an account of the intensity of the data science market as well as the tools that are involved there.
Data science platforms have a wide range of applications when it comes to new and emerging businesses. It also caters to the requirements of the established business and helps them to derive state-of-the-art analytics. In this article, we aim to understand the entire morphology and life cycle of data science platforms in detail.
Data science platform: Morphology
In simple terms, we can define a data science platform as a technical framework that enables us to understand the working of the data science life cycle. In most cases, the data science life cycle revolves around three important stages. The first stage is called the stage of data integration. Needless to mention, the data that is fed into this stage is already processed and cleansed. It is structured in format. The second stage is all about the development of the data science model that caters to the requirements and the given set of parameters. The third stage is related to the deployment of the data science model that has been developed in the second stage.
With the help of a data science platform, we can effectively track the changes in the life cycle of the data science project that we are working with. This helps us to derive analytics with a lot of ease. The deployment of models also requires a rapid pace using the data science platform.
Classification of data science platforms
There are two types of data science platforms. The first is called the open type of data science platform and the second is called the closed type. In the open-type platform, data scientists are given the freedom to choose the programming language that they like. There are also a variety of tools and techniques that can be experimented with the data science platform which is open type. On the other hand, the closed type data science platform does not provide the customization that is available in the open type. In simple terms, we are forced to use the programming language that is provided by the vendor in the closed type data science platform. The graphical user interface and the set of tools are also restricted in nature.
What challenges arise for data scientists?
Data scientists often start their projects with data collection and processing. They often face coordination challenges and end up working on the same problem as their counterparts.
Data scientists need to test their ideas in relation to the problem statement in a number of ways. This is not a simple process but requires a lot of repetitions and iterations. Needless to mention, this can slow down the progress of a data science project in a number of ways. An effective data science platform acts as a counter mechanism to the slow-down process by providing new pathways to the problem statement.
Counter mechanism
A data science project not only enables better coordination among data scientists but also ensures that the progress of the entire project is kept on track. Data science platform also minimizes the manual reports that are required by data scientists for automating different stages of the data science lifecycle. In this way, the research and development related to data science projects are accentuated. Finally, the data science platform also allows us to make last-minute adjustments and modifications in our solution to the problem statement. It also allows us to make necessary alterations in the life cycle of data science. In this way, a data science platform provides one of the most flexible and amendable ways in which we can solve the problem statement and accomplish the data science lifecycle in the easiest possible way.
Exemplifying it
There are a lot of data science platforms that are popular among the data scientists. Wolfram data science platform is the best among the lot. The data science platform provided by Gathr has become extremely popular in a short span of time. The data science platform provided by Domino lab also has a lot of following worldwide.
Concluding remarks
Although a large number of companies prefer the Google Cloud data science platform, other platforms have also made their mark in the data science market. In one word, the data science market has a lot of scope for other players that are entering into it.
