Data are becoming the new raw material of business
The Economist


Data Science in 30 Minutes: Building Data Science Capabilities That Scale with DataScience.com CEO, Ian Swanson

This FREE webinar will be on May 17th at 5:30 PM ET. Register below now, space is limited!

Join The Data Incubator and DataScience.com CEO, Ian Swanson  May 17th at 5:30 PM, LIVE online, for the next installment of our free online webinar series, Data Science in 30 minutes: Building Data Science Capabilities That Scale.

Data scientists and machine learning engineers saw the highest job growth of any role last year, yet few companies have successfully turned their aggressive hiring into profitable, scalable data science capabilities. In this session, DataScience.com CEO Ian Swanson shares lessons learned from building a platform that supports collaborative data science for a variety of clients, from startups to Fortune 500 companies. Learn about the technology gaps, roadblocks to innovation and efficiency, and talent retention challenges that have proven to be detrimental to data science success in an enterprise environment — and how to mitigate them.
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Data Science in 30 Minutes: Alan Schwarz, Former NYTimes Journalist, on Numbers-Based Journalism

Alan Schwarz, former NY Times journalist joined The Data Incubator for the February 2018 installment of our free online webinar series, Data Science in 30 minutes: Numbers-Based Journalism.

Sign up below to get access to the video of this webinar for free!

Alan Schwarz, former N.Y. Times investigative reporter and Pulitzer finalist, discussed numbers-based journalism that shook industries from the National Football League to Big Pharma. Alan used data analysis to expose the NFL’s cover-up of concussions as well as issues in child psychiatry.
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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 in January 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 17th 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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