�ݺ�ߣshows by User: foco24

�ݺ�ߣshows by User: foco24 / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: foco24 / Thu, 14 Jul 2016 16:46:02 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: foco24 Practical Tips On Handling Big Data /slideshow/practical-tips-on-handling-big-data/64032852 pp-160714164602
Big Data is often shrouded in mystery and jargon. This talk will attempt to demystify the topic through a series of short vignettes on how to pragmatically deal with Big Data. Including: how to avoid Big Data problems in the first place, hardware optimizations, and scaling code through functional programming Bio: Dr. Brian Spiering is a professor of Data Science at Galvanize University, which is industry-driven, outcomes-focused education institution offering a Masters in Data Science. He teaches Natural Language Processing (NLP), Data Engineering, and Deep Learning.]]>
Big Data is often shrouded in mystery and jargon. This talk will attempt to demystify the topic through a series of short vignettes on how to pragmatically deal with Big Data. Including: how to avoid Big Data problems in the first place, hardware optimizations, and scaling code through functional programming Bio: Dr. Brian Spiering is a professor of Data Science at Galvanize University, which is industry-driven, outcomes-focused education institution offering a Masters in Data Science. He teaches Natural Language Processing (NLP), Data Engineering, and Deep Learning.]]> Thu, 14 Jul 2016 16:46:02 GMT /slideshow/practical-tips-on-handling-big-data/64032852 foco24@slideshare.net(foco24) Practical Tips On Handling Big Data foco24 Big Data is often shrouded in mystery and jargon. This talk will attempt to demystify the topic through a series of short vignettes on how to pragmatically deal with Big Data. Including: how to avoid Big Data problems in the first place, hardware optimizations, and scaling code through functional programming Bio: Dr. Brian Spiering is a professor of Data Science at Galvanize University, which is industry-driven, outcomes-focused education institution offering a Masters in Data Science. He teaches Natural Language Processing (NLP), Data Engineering, and Deep Learning. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pp-160714164602-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Big Data is often shrouded in mystery and jargon. This talk will attempt to demystify the topic through a series of short vignettes on how to pragmatically deal with Big Data. Including: how to avoid Big Data problems in the first place, hardware optimizations, and scaling code through functional programming Bio: Dr. Brian Spiering is a professor of Data Science at Galvanize University, which is industry-driven, outcomes-focused education institution offering a Masters in Data Science. He teaches Natural Language Processing (NLP), Data Engineering, and Deep Learning.

Practical Tips On Handling Big Data from Brian Spiering

]]> 7233 4 https://cdn.slidesharecdn.com/ss_thumbnails/pp-160714164602-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 A Data Science Workflow: Nonprofit Edition /slideshow/a-data-science-workflow-nonprofit-edition/58983274 datakindbrianspiering-160302185718
This is a proposed workflow for data science projects and a specific use case where that framework helped a nonprofit and DataKind complete a successful data science project. Data Science Workflow: 1. Ask 2. Acquire 3. Process 4. Model 5. Deliver Dr. Brian Spiering is a Data Science Faculty member at GalvanizeU which, in cooperation with the University of New Haven, offers an accredited Master’s degree program in Data Science. ]]>
This is a proposed workflow for data science projects and a specific use case where that framework helped a nonprofit and DataKind complete a successful data science project. Data Science Workflow: 1. Ask 2. Acquire 3. Process 4. Model 5. Deliver Dr. Brian Spiering is a Data Science Faculty member at GalvanizeU which, in cooperation with the University of New Haven, offers an accredited Master’s degree program in Data Science. ]]> Wed, 02 Mar 2016 18:57:18 GMT /slideshow/a-data-science-workflow-nonprofit-edition/58983274 foco24@slideshare.net(foco24) A Data Science Workflow: Nonprofit Edition foco24 This is a proposed workflow for data science projects and a specific use case where that framework helped a nonprofit and DataKind complete a successful data science project. Data Science Workflow: 1. Ask 2. Acquire 3. Process 4. Model 5. Deliver Dr. Brian Spiering is a Data Science Faculty member at GalvanizeU which, in cooperation with the University of New Haven, offers an accredited Master’s degree program in Data Science. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/datakindbrianspiering-160302185718-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> This is a proposed workflow for data science projects and a specific use case where that framework helped a nonprofit and DataKind complete a successful data science project. Data Science Workflow: 1. Ask 2. Acquire 3. Process 4. Model 5. Deliver Dr. Brian Spiering is a Data Science Faculty member at GalvanizeU which, in cooperation with the University of New Haven, offers an accredited Master’s degree program in Data Science.

A Data Science Workflow: Nonprofit Edition from Brian Spiering

]]> 518 7 https://cdn.slidesharecdn.com/ss_thumbnails/datakindbrianspiering-160302185718-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Introduction to Applied Machine Learning in R /slideshow/introduction-to-applied-machine-learning-in-r/45564193 talk2015-03-07-150307215326-conversion-gate01
Introduction to Applied Machine Learning in R by Dr. Brian J. Spiering What is applied machine learning? Machine learning is programming computers to automatically learn and generalize from examples. It allows for predictions or decisions beyond explicit programming of rules. Applying machine learning is a force multiplier for analysts, given the rise in the volume and velocity of data. This talk will the cover the basic, practical applications of machine learning in common contexts using the R programming language. Presented at Bay Area Entrepreneurs in Statistics]]>
Introduction to Applied Machine Learning in R by Dr. Brian J. Spiering What is applied machine learning? Machine learning is programming computers to automatically learn and generalize from examples. It allows for predictions or decisions beyond explicit programming of rules. Applying machine learning is a force multiplier for analysts, given the rise in the volume and velocity of data. This talk will the cover the basic, practical applications of machine learning in common contexts using the R programming language. Presented at Bay Area Entrepreneurs in Statistics]]> Sat, 07 Mar 2015 21:53:25 GMT /slideshow/introduction-to-applied-machine-learning-in-r/45564193 foco24@slideshare.net(foco24) Introduction to Applied Machine Learning in R foco24 Introduction to Applied Machine Learning in R by Dr. Brian J. Spiering What is applied machine learning? Machine learning is programming computers to automatically learn and generalize from examples. It allows for predictions or decisions beyond explicit programming of rules. Applying machine learning is a force multiplier for analysts, given the rise in the volume and velocity of data. This talk will the cover the basic, practical applications of machine learning in common contexts using the R programming language. Presented at Bay Area Entrepreneurs in Statistics <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/talk2015-03-07-150307215326-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Introduction to Applied Machine Learning in R by Dr. Brian J. Spiering What is applied machine learning? Machine learning is programming computers to automatically learn and generalize from examples. It allows for predictions or decisions beyond explicit programming of rules. Applying machine learning is a force multiplier for analysts, given the rise in the volume and velocity of data. This talk will the cover the basic, practical applications of machine learning in common contexts using the R programming language. Presented at Bay Area Entrepreneurs in Statistics

Introduction to Applied Machine Learning in R from Brian Spiering

]]> 1752 6 https://cdn.slidesharecdn.com/ss_thumbnails/talk2015-03-07-150307215326-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 https://cdn.slidesharecdn.com/profile-photo-foco24-48x48.jpg?cb=1629741201 Galvanize is reinventing contemporary education through a network of urban campuses that combine outcome driven technical courses alongside a community of entrepreneurs and startups. We provide access to the skills, network and talent individuals and companies need to be competitive, all under one beautiful roof. **We are HIRING! Seeking Instructors for Full Stack Web Development, Instructors for Data Science and Instructors for Data Engineering at all Levels, across all campuses. Our locations include: Denver CO(HQ), Boulder CO, Fort Collins CO, San Francisco CA, Seattle WA and coming soon, Austin TX. If you want to learn more about Galvanize, reach out to me. I would love to chat! I... http://brianspiering.com https://cdn.slidesharecdn.com/ss_thumbnails/pp-160714164602-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/practical-tips-on-handling-big-data/64032852 Practical Tips On Hand... https://cdn.slidesharecdn.com/ss_thumbnails/datakindbrianspiering-160302185718-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/a-data-science-workflow-nonprofit-edition/58983274 A Data Science Workflo... https://cdn.slidesharecdn.com/ss_thumbnails/talk2015-03-07-150307215326-conversion-gate01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/introduction-to-applied-machine-learning-in-r/45564193 Introduction to Applie...