Data Science Use Cases in Industry and Demonstration of RapidminerSpeaker 1: Ralf Klinkenberg
Abstract: A short description of the state, challenges, barriers, use cases, and opportunities of Industrial Data Science and the Cross-Industry Standard Process for Data Mining (CRISP-DM) focussing on industrial data science applications and best practices.
Ralf Klinkenberg (@RalfKlinkenberg) is the Co-Founder and Head of Data Science Research at RapidMiner. Ralf is a data scientist and consultant with more than 20 years of experience in data mining and predictive analytics. He is holding a Master of Science degree from Technical University of Dortmund in Germany and Missouri University of Science and Technology in the USA, and worked as a machine learning researcher at both universities. He co-founded both, the open source data mining project RapidMiner, as well as the company RapidMiner, for which he currently serves as Head of Data Science Research. Ralf Klinkenberg has more than 20 years of experience in machine learning, data mining, text mining, web mining, predictive analytics, and their applications in diverse sectors and use cases
Abstract: A demonstration of Rapidminer, an easy tool for Data Scientists and Researcher focusing on how to build efficient Machine Learning Models, Algorithms and to deploy the same for the enterprise.
Kamal Pradhan is the Senior Data Scientist, specializing in the area of Machine Learning & Deep Learning. He has deep industry experience and currently working in Aptus Data Labs in implementing various business use cases on data mining and predictive analytics for large-scale enterprises. He is a graduate in Computer Engineering from Sambalpur University and Data Science researcher. He is a certified Analyst and Expert on Rapidminer Data Science tool and products.