Successful data science and analytics projects should focus on the explanation and delivery of business impact. That requires strong collaboration between business analysts and data scientists.
Preparing rich representative data from Hadoop and other sources is important, but data discovery and predictive analytics are equally valuable. That’s what helps team members quickly evaluate which events are drivers or inhibitors of success – and helps accurately predict future outcomes.
Throughout the course of a project, how can organizations get value from Hadoop and other relevant data sources? How can business analysts stay focused on the most important details? And how can data scientists prototype and implement models in a quick, productive and easy-to-use manner?
In this webinar, our experts will use SAS® Visual Analytics and SAS Visual Statistics to teach you how to:
- Quickly identify predictive drivers.
- Discover outliers using interactive tools.
- Use drag-and-drop features to build predictive models.
- Simultaneously build models and process results for each group or segment of data.
- Visually explore predictive outputs or values.
- Compare models and apply them to new data.
Wayne Thompson is the Chief Data Scientist at SAS and a globally renowned presenter, teacher, practitioner and innovator in the fields of data mining and machine learning. He has worked alongside the world's biggest and most challenging organizations to help them improve performance with analytics. Over the course of his 20-year tenure at SAS, he has been credited with bringing to market several landmark technologies. His current focus includes easy-to-use, self-service data mining tools for business analysts, outlier detection and description, entity analytics and recommendation engines with a heavy focus on SAS highly interactive in-memory analytics optimized for Hadoop.
Tapan Patel is a Product Marketing Manager at SAS. With more than 15 years in the enterprise software market, Patel leads global marketing efforts at SAS for business intelligence, predictive analytics, and in-memory analytics. Specifically, he leads go-to-market initiatives for SAS Visual Analytics and SAS Visual Statistics. He works closely with customers, partners, industry analysts, press/media and thought leaders to ensure that SAS continues to meet customer requirements and deliver high-value solutions.