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DATA SCIENCE AND BIG DATA ANALYTICS CENTERS IN CALICUT

Data is coming at an exponentially increasing rate, from an explosion of data sources. But the amount of time you have available to do something with that data is shrinking. Develop a data strategy to realise fast business outcomes - our experts, partners and technology can help you succeed in a data-driven world. The explosion of data and data-collecting devices offers great opportunities. But to take full advantage, you need faster computing in the data center and intelligent edge technologies.

A big data science analyst's main objective is to organize and analyze large amounts of data, often using software specifically designed for the task. The result of the analysis needs to be simple enough for all invested stakeholders to understand - especially those working outside of IT.

A data scientist's approach to data analysis depends on their industry and the specific needs of the business or department they are working for. Before a data scientist can find meaning in structured or unstructured data, business leaders and department managers must communicate what they're looking for. As such, a data scientist must have enough business domain expertise to translate company goals into data-based deliverables such as prediction engines, pattern detection analysis or even optimization algorithms.

Certified Big Data Science Analyst (CBDSA) course covers the concept of Business Analytics and Big Data technologies with its strategic importance to any organization. Participants will be introduced to the concept of business analytics with big data technologies. The course deals with basic principles, concepts, and techniques/tools used for big data and business analytics, which includes data mining, Hadoop, HDFS & MapReduce. Also, this course covers different types of business analytics with real life use cases including association rule mining and regression models. Participants will get good picture of all these concepts and how they all are inter connected to each other in organizational context.

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