Financial Analytics Fundamentals Use Data Science And Analytics In BFSI

For a data analyst, financial analytics is one of the most useful skill sets to inculcate given the rapid growth of roles in this booming sector. A specialisation in this field will be a value-add given the rapid rise of fintech in India’s digital ecosystem. Financial analytics helps companies uncover deeper insights into the company’s finances and can help in finding pain points and new streams of revenue. Financial Analysts work in investment firms, banks, insurance sector and digital payments companies.
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Spotlight

OTHER ON-DEMAND WEBINARS

FTC Crackdown on Data Flows- What it means and How to Comply

In the aftermath of Roe v Wade, the FTC shared a blog post declaring its commitment to crack down on illegal data usage & data sharing by software organizations.
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Journey to the Cloud, Self-Service BI, and Data Lakes with Data Virtualization

denodo

Denodo DataFest, the premier agile data management and analytics conference, returned once again to New York and London in 2018. Our annual user conference brings together industry analysts, subject matter experts, business leaders, data management leaders to discuss data strategies to enable a successful journey to the cloud, self-service BI, and data lakes with data virtualization. The event invited attendees to watch demos, hear customer success stories, and educate themselves on the best practice implementations of data virtualization.
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How to Operationalize Data Mapping for Engineering

In this webinar, we will discuss the importance of data maps in building a privacy program. We look at the gap between privacy and engineering teams, the problems of scaling privacy programs with a business process approach to data mapping, and how an application-led approach helps you develop dynamic & complete data maps.
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DataOps and Cloud Data Ecosystems: Overcoming the 3 Biggest Hurdles

The cloud is changing the way we use data and at the same time, the way we use data is changing the cloud. Organizations are adopting multiple cloud data platforms to maximize data’s value, but in doing so are often increasing complexity for data engineering and DataOps teams that may stifle the very reason for investing in those platforms.
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