As data is rapidly becoming the catalyst for driving innovation, the world is seeing growing adoption of data analytics/Artificial Intelligence technologies.
Today, there is an evolving paradigm shift from "Web and mobile first" to "AI first". Enterprises are investing massively in data analytics/Artificial Intelligence technologies to drive innovation in order to gain competitive advantage.
However, the foundation of any data analytics and Artificial intelligence is the data infrastructure and pipeline. The first 3 steps of the 6-steps AI hierarchy of need emphasizes the need to build the right data infrastructure. This means that the effort of analytics/Artificial Intelligence will be a complete effort in futility if the data infrastructure is not done right.
In this presentation, Kelvin will be highlighting the importance of building that right data infrastructure to support any analytics/AI initiative.
He will further discuss the foundational concepts and tools needed to build a robust and scalable data infrastructure - data warehouse/data lakes as well as the pipelines to consolidate data into the infrastructure. Further, we will discuss the pros and cons of the various data engineering tools, making choices between proprietary and open-source solutions such as Hadoop, Python, etc.
Speaker will conclude with a practical end-to-end data engineering use-case showing how enterprises can implement/build the right data infrastructure.
Below are key highlights of the talk:
1. Understanding the importance of building the right data infrastructure
2. The AI hierarchy of need
3. Foundational concepts in data engineering (data warehouse, data lake, and data pipelines).
4. Choosing the right tools/technology to build your data infrastructure - Pros & Cons.
5. Open source versus proprietary solutions
6. End-to-end data engineering use-case.
Data Scientist at World Bank
Daynan is currently a Data Science Consultant with the World Bank and is investigating ways remote sensing can provide insight into economic activity, particularly during the COVID-19 pandemic in countries that have low levels of conventional economic reporting.
Daynan’s early career spanned technology and public policy leading to a role as a Sr. Policy Advisor in the New York City (NYC) Mayor’s Office, where he oversaw the implementation of neighborhood resilience programs following Hurricane Sandy in 2012. During this experience, he recognized the power of data science to understand complex systems and pursued an M.S. in Applied Urban Science and Informatics from New York University.
Following this he drew from his career experience to focus on data science applications for urban systems and the built environment. He was the Director of Strategy and Performance for the NYC Department of Information Technology and Telecommunications. After that, and prior to working with the World Bank, Daynan was a Sr. Data Scientist at GeoPhy, a real estate data company, where he contributed to the development of the firm's models and methodology and led several research and development efforts in applied machine learning.
Daynan serves on the board for 100cameras, a non-profit dedicated to teaching photography, and lives in New York City with his wife. When he's not data science-ing, you will often catch him talking about cities, music, stand-up comedy, and astronomy. But not pie graphs.
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