Technology Handbook: What are The 4 Pillars of Data Science in 2019?

Technology Handbook: What are The 4 Pillars of Data Science in 2019?

In the last two years, we have seen a frantic meshing of virtual and physical worlds connected by data and computing. Today, data centers have been largely replaced by department-level enterprise Data Science teams led by experienced and fresh graduates.

In this article, we tell you about the four pillars of Data Science.

1.Data Science is now a Mainstream
Together with IT and Revenue Generation, Data Science has become the key ROI factor in the way businesses operate today. The acceleration in data processes and analytics have opened up new fields of opportunities arising from adoption of newer technologies, such as Artificial Intelligence (AI), Machine Learning, Robotic Process Automation, Edge Computing, Blockchain and Big Data Analytics. For businesses are to expect any return on their investments into these technologies, it’s very important to build a powerful in-house proprietary Data Science Analytics team.

2.The Unique Features of Data Science Engine
In the Data Science courses in Bangalore, professionals are taught to identify, analyze and apply the various features of Big Data platform and Big Data Analytics software.
The unique features that you could learn are:

  • Hadoop/ Apache Spark Architecture; To understand how Big Data storage and mining could save energy, space and time associated with processing of limitless volume of data
  • Streamlined Content Management; To minimize time spent in understanding content and document workflow
  • Data Ingestion, Integration and Governance; To provide effective comprehensive protocol on collection, management and security of data, protected against external and internal risks

3. Integrations of AI and Machine Learning Automation
The highest percentage of Data Science management is achieved with AI and Machine Learning. The new generation of human workforce handling Big Data and associated analytics are tech-savvy and rely on automation to deliver on results. Today’s data analysts are expected to collect, mine and analyze powerful set of data within hours, if not minutes. This can be achieved with machine learning algorithms that can analyze unstructured data without high-level supervision.

4. Data Science Software and Technology Leaders
It is important to keep a tab on the leaders in the space. They are leading the pack by virtue of their technology investments, hiring volume and dynamic innovation roadmap. Consultants and advisors in the industry watch these companies to ascertain where Data Science is slated to turn next.
The fourth pillar involves learning about companies that are heavily invested into this space.

The Data Science Courses in Bangalore have become the largest source of information and talent, providing high-quality professionals to leading businesses. As the demand for high-quality professionals in Data Science continue to grow, it is essential to understand the foundation and the pillars of this industry.

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