How To Operationalize Data Science Projects

Today’s data scientists use machine learning to predict trends, plan ahead of demand and events, and uncover patterns and behaviors. But they spend a significant amount of time on data engineering rather than actual data analysis, delaying the operationalization of machine-learning projects. Find out how you can solve this challenge and help your data scientists be more productive. About our speaker: Dr. Engin Cukuroglu is a data scientist at Hitachi Vantara, Singapore. He received his BS in chemical and biological engineering and has a MS and a doctorate in computational science and engineering. His studies primarily focused on data analytics and big data in protein-protein interactions. After getting his degrees, he analyzed genomic data and developed pipelines for cancer and stem-cell research. His findings have been published in peer-reviewed journals. Dr. Cukuroglu is currently working with rail operators, mining companies, manufacturers, banks, fleet operators and delivery partners in Asia-Pacific to enable their machine-learning and predictive-analytics strategies.