Ensembling is one of the hottest techniques in today’s predictive analytics competitions. Every single recent winner of Kaggle.com and KDD competitions used an ensemble technique, including famous algorithms such as XGBoost and Random Forest. This session will provide a detailed overview of ensemble models, their origin, and show why they are so effective.
Data Governance is one of the hottest topics in data management, focusing both on how Governance driven change can enable companies to gain better leverage from their data and also to help them design and enforce the controls needed to ensure they remain compliant with regulations. Despite this rapidly growing focus, many Data Governance initiatives fail to meet their goals. This session will outline why Data Governance and Architecture should be connected, how to make it happen, and what part Business Intelligence and Data Warehousing will play in defining a robust and sustainable Governance programme.
Migrating an existing data warehouse to the cloud is a complex process of moving schema, data, and ETL. The complexity increases when architectural modernization, restructuring of database schema or rebuilding of data pipelines is needed. In this session Dave Wells provides an overview of the benefits, techniques, and challenges when migrating an existing data warehouse to the cloud.
Praktische workshop met Keith McCormick over het toepassen van supervised en unsupervised machine learning. Hoe zet je business vraagstukken om in bruikbare machine learning modellen? Welke modellen zijn het meest geëigend voor welke toepassing? Hoe zijn deze modellen op creatieve wijze te combineren? U gaat zelf met oefeningen aan de slag.
Organizations worldwide are facing the challenge of effectively analyzing their exponentially growing data stores. Most data warehouses were designed before the big data explosion, and struggle to support modern workloads. To make due, many companies are cutting down on their data pipelines, severely limiting the productivity of data professionals.