Data are becoming the new raw material of business
The Economist


Data Science in 30 Minutes: Kirk Borne – A Fortuitous Career in Data Science


Booz Allen Hamilton’s Kirk Borne joined The Data Incubator in August for our FREE monthly webinar series, Data Science in 30 minutes!

Kirk Borne took us on a journey through his career in science and technology, explaining how the industry – and himself – have evolved over the last 4 decades. Starting with skipping lunches in high school to a systematic twitter obsession, Kirk shed light on his road to success in the data science industry.
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Data Science in 30 Minutes: Scikit-Learn with Core-Contributor Andreas Mueller


scikit-learn‘s Andreas Mueller joined The Data Incubator in December 2017 for our FREE monthly webinar series, Data Science in 30 Minutes!

We talked about everything new in 0.19, that got released in July of this year, and what the plans are for 0.20 that will be released early next year. Highlights are the multiple metric grid-search, faster T-SNE and better handling of categorical and mixed data.
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Data Science in 30 Minutes: A Conversation with Gregory Piatetsky-Shapiro, President of KDnuggets


KDnuggets’ Gregory Piatetsky-Shapiro, Ph.D  joined The Data Incubator on January 11th for the first 2018 installment of our free online webinar series, Data Science in 30 minutes! Gregory discussed his career – from Data Mining to Data Science and examine current trends in the field.

From Data Mining to Knowledge Discovery to Data Science: Gregory Piatetsky talked about his pioneering career in data science, including founding KDnuggets, and co-founding KDD Conferences and ACM SIGKDD, and examined current trends in the field, Data Science Automation, citizen Data Scientists, and implications of AI.
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Data Science in 30 Minutes: Infrastructure for Usable Machine Learning with Spark Creator and Stanford Professor, Matei Zaharia

This FREE webinar will be on April 3rd at 5:30 PM ET. Register below now, space is limited!

Join The Data Incubator and Databricks co-founder, Matei Zaharia, Ph.D for the next installment of our FREE online webinar series, Data Science in 30 minutes: Infrastructure for Usable Machine Learning.

Despite incredible recent advances in machine learning, building machine learning applications remains prohibitively time-consuming and expensive for all but the best-trained, best-funded engineering teams. This expense usually comes not from a need for new and improved statistical models but instead from a lack of systems and tools for supporting end-to-end machine learning application development, from data preparation and labeling to productionization and monitoring. In the Stanford DAWN project, we are developing a set of tools to make these processes easier, from weak supervision approaches to dramatically reduce the need for labeled data, to query-specific model specialization to reduce serving cost, and end-to-end ML systems that encapsulate a complete task and greatly simplify the interface to the user.
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