Certified Data Scientist knows the importance of data science platforms!

Data Science has been making waves in the IT industry since the day it was coined. With the kind of disruptions, the data science field of data science is experiencing and the disruptions it has been creating has made the job of data science professionals a lot easier.

Data scientist these days, especially the certified data scientist knows the variety of data science tools and data science platforms available to her both free and for purchase from the market.

Before we learn about the importance of data science tools and platforms let’s understand what are data science platforms?

Put it simply – a data science platform is a nothing but a software hub where all the data science related work is done. The work comprises of integrating and exploring data from various sources, coding and building models which will leverage that data, deploy those models into production, and serve up results, whether through model-powered applications or reports.

Interestingly, a recent study conducted by MarkesandMarkets has predicted the data science platforms’ market will rise up to $101.4 billion by the year 2021. However, the space is relatively new so there is more confusion than clarity about data science platforms – what components will compromise a data science platform and the most important question is how do you know whether it will be worth investing in one for your organization?

A million-dollar question right? Here’s what you need to look for in data science platforms before you think about investing in them.

1. Remember that a good data science platform should offer your data scientists the building blocks that will help them create a solution to any data science problem.
2. Good data science platform should also offer your data scientists with an environment where they are able to incorporate the solutions into your businesses process and products.
3. Offering all the support needed to a data scientist professional – especially when carrying out data and analytics – is another feature of good data science platforms.
4. When data science platforms facilitate the easy accomplishment of tasks like visualization, interactive exploration, performance engineering data preparation, deployment and data access – you will know that you have invested in a good data science platform.
5. Data scientists all over the world seek data science platforms that will let them work both online and offline and with the introduction of cloud-based data science platforms, certified data scientists are now able to work with their data on any Internet-enabled device.

In addition, sharing the components of the work with the colleagues or even collaborate with other employees without any security-related tension is also another feature of good data science platforms.   

In short, remember that data science platforms should support all the four stages of data science production line –

1. Data preparation
2. Model development
3. DevOps
4. Business delivery

Also, another thing to remember here is that data science platforms are dependent on transparent data access, along with consistent metadata, strong enterprise governance, automated machine learning, and model building, operationalized model management, and data science tools, which measure and improve its impact on businesses.

The importance of data science platforms has grown recently because of the following reasons –

1. Data Science became critical for business success and certified data scientists know that with good data science platforms the works become easy and efficient.
2. Exponential growth of data science tools has led to the sudden surge in data science platforms.
3. Tremendous growth in data science models and model creators has resulted in data science platforms.
4. A shift in power from IT to business has also led to the growth of data science platforms.

With the rise of data-driven organizations, the demand for data science professionals is also on the rise, which in turn has given birth to increased demand for data science platforms.


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