A data science platform is a hub of software that allows data scientists to strategize plan, discover actionable insights from data, and connect those insights all over an enterprise in a single environment. A data science platform includes a variety of technologies for machine learning and other state-of-the-art analytics uses. The data science platform benefits businesses to make data-driven decisions to enhance customer satisfaction and maximize their output. As technology is advancing and developing, the data science platform provides the team of data scientists better scalability and flexibility by adding the latest tools of data science to the inventory.
Importance of Data Science Platform for Companies
In any organization, there are data science teams who use some kind of a software platform to enhance their workflow. For instance, ticketing systems are used by the customer support team, and the sales team uses CRM systems. Similarly, organizations need to depend on data science platforms to perform data science at scale. Now, it’s time for the companies to say goodbye to data science processes that are dependent on widespread engineering effort and disjointed tools to perform data science. Data science platforms can bring everything needed by the data science team at a unified place. This helps the data scientist to effectively team up and pool resources by speeding up the process.
Types of Data Science Platform
Data scientists can enhance their analysis with the help of a data science platform by helping them track, run, reproduce, deploy, and share analytical models faster. Typically, all these tasks entail a lot of engineering efforts and hassle to maintain and build analytical models. But an extra power tools is given by the data science platform to the data scientists to speed up analysis. This platform helps teams of data science to stand tall in the competitive race to effectively leverage analytics.
Data science platforms are classified as follows:
- Open Data Science Platform
This type of platforms is the one that provides flexibility to data scientists to choose the packages and programming languages they want to use as and when they require. An open data science platform lets data scientists use the right tools for the right job grounded on the situation and also allows them experiment with different tools and languages.
- Closed Data Science Platform
In this type of platform, the data scientists have to use the seller’s platform specific programming language, modelling packages, and GUI tools. This limits data scientists to use the tools on top of the platform.
Data Scientist Vs Machine Learning Engineer
It’s important to understand that as data fields and technologies grow, careers may very well. Technology careers often interconnect, but the difference between a data scientists and machine learning engineer is important to distinguish.
- Skills Needed for Data Scientists
Data mining and cleaning, statistics, data visualization, programming languages such as Python and R, unstructured techniques of data management technique, understanding of SQL databases, and knowledge of big data tools like Hive and Pig, Hadoop.
- Skills Needed for Machine Learning Engineers
Statistical modelling, computer science fundamentals, data modelling and evaluation, applications and understanding of algorithms, data architecture design, natural language processing, and text representation techniques.
Data science has a massive potential and it is a rapidly growing and broad field that harnesses the processing power and widespread amounts of available data to gain insights. Whereas, machine learning is one of the most exciting technologies in modern data science. It allows computers to learn autonomously from the available data.
Data Science Platform Features
It is important to have a centralized location for data science work. Typically, data science projects involve many different tools designed for each step of the process. On the behalf of DataScience.com, Forrester Consulting conducted a study which revealed that tool sprawl is one of the most common challenge for data science teams. The entire data modelling process is put by a data science platform so that the data science teams can focus on deriving insights from data and communicate those insights to the key stakeholders in the business. Features like streamlined model deployment and project-based organization help make this work instinctive.
A good data science platform is valuable as it explores the existing data on large machines without the engineering setup or intervention of the devOps. It helps the data scientists to easily understand the past work of his colleagues without any need of beginning from the scratch. A good data science platform also makes the process easier for the data scientists to track the work and reproduce it easily. It allows data scientists to easily publish models as API’s so that it can be easily used with the systems in other programming languages without any additional re-implementation engineering effort.
Conclusion
In today’s world, data Science platform has become a crucial need for business. A huge amount of data is produced today, and with the use of data science tools, businesses can be carried out in a better way. A data Science platform is helping us in many fields such as information technology, healthcare & life sciences, manufacturing, research, BFSI, and energy & utilities. The global data science platform market is anticipated to rise at a CAGR of 31.1% for the next 5 to 7 years. Though data science platform is helping us in many fields, yet there is an acute shortage of workforce to perform the task.
