hilary_mason_data hilary_mason_data-2011 hilary_mason_data-2011-59 knowledge-graph by maker-knowledge-mining
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Introduction: Uses This Posted: July 29, 2011 | Author: Hilary Mason | Filed under: Media | Tags: tools , usesthis | 1 Comment » I’m honored to have my tools of choice featured on Uses This !
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same-blog 1 0.99999994 59 hilary mason data-2011-07-29-Uses This
Introduction: Uses This Posted: July 29, 2011 | Author: Hilary Mason | Filed under: Media | Tags: tools , usesthis | 1 Comment » I’m honored to have my tools of choice featured on Uses This !
2 0.093288973 31 hilary mason data-2009-08-12-My NYC Python Meetup Presentation: Practical Data Analysis in Python
Introduction: My NYC Python Meetup Presentation: Practical Data Analysis in Python Posted: August 12, 2009 | Author: hilary | Filed under: blog | Tags: data , data analysis , nltk , presentations , python , spam , twitter | Leave a comment » I gave a talk at the NYC Python Meetup on July 29 on Practical Data Analysis in Python . I tend to use my slides for visual representations of the concepts I’m discussing, so there’s a lot of content that was in the presentation that you unfortunately won’t see here. The talk starts with the immense opportunities for knowledge derived from data. I spent some time showing data systems ‘in the wild’ along with the appropriate algorithmic vocabulary (for example, amazon.com ‘s ‘books you might like’ feature is a recommender system ). Once we can describe the problems properly, we can look for tools, and Python has many! Finally, in the fun part of the presentation, I demoed working code that uses NLTK to build a Twitter sp
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Introduction: Help, I’m the first data scientist at my company! Posted: September 21, 2012 | Author: Hilary Mason | Filed under: blog | Tags: datagotham , datascience , panel , presentations | 2 Comments » I moderated a panel at DataGotham with Adam Laiacano from Tumblr , Fred Benenson from Kickstarter , and Roberto Medri from Etsy about being the first data scientist at a company. We covered everything from what people’s job responsibilities are, the tools they use, successes, failures, how they are integrated into an organization, and how they have hired other data scientists to join them. The panelists were concise, articulate, and intelligent. Watch it below!
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Introduction: Should you attend Hadoop World? Yes. Posted: August 15, 2010 | Author: hilary | Filed under: blog | Tags: conferences , hadoop , hadoopworld , questions | 2 Comments » I received this e-mail via my contact form : I just discovered you via a Google search because I’m highly considering attending this year’s upcoming Hadoop World in NYC. I appreciate your page that you wrote up after attending last year’s event. I’m wondering if you feel that Hadoop has enough momentum and support to be a “here to stay” technology worth investing one’s time and education into, or is it possible it might fade and be deprecated by something else as the need for big data analysis continues to grow? … I’ve had a few similar conversation with people lately, and I thought posting my response might help others making similar decisions. The e-mail is referencing my post from last year’s hadoop world NYC . Thanks for reaching out. There are several questions in your messa
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Introduction: What Mugshots Mean For Public Data Posted: October 6, 2013 | Author: Hilary Mason | Filed under: blog | Tags: data , mugshots , privacy | 20 Comments » The New York Times has a story this morning on the growing use of mugshot data for, essentially, extortion . These sites scrape mugshots off of public records databases, use SEO techniques to rank highly in Google searches for people’s names, and then charge those featured in the image to have the pages removed. Many of the people featured were never even convicted of a crime. What the mugshot story demonstrates but never says explicitly is that data is no longer just private or public, but often exists in an in-between state, where the public-ness of the data is a function of how much work is required to find it. Let’s say you’re actually doing a background check on someone you are going on a date with (one of the use cases the operators of these sites claim is common). Before online systems, you c
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Introduction: Uses This Posted: July 29, 2011 | Author: Hilary Mason | Filed under: Media | Tags: tools , usesthis | 1 Comment » I’m honored to have my tools of choice featured on Uses This !
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Introduction: My NYC Python Meetup Presentation: Practical Data Analysis in Python Posted: August 12, 2009 | Author: hilary | Filed under: blog | Tags: data , data analysis , nltk , presentations , python , spam , twitter | Leave a comment » I gave a talk at the NYC Python Meetup on July 29 on Practical Data Analysis in Python . I tend to use my slides for visual representations of the concepts I’m discussing, so there’s a lot of content that was in the presentation that you unfortunately won’t see here. The talk starts with the immense opportunities for knowledge derived from data. I spent some time showing data systems ‘in the wild’ along with the appropriate algorithmic vocabulary (for example, amazon.com ‘s ‘books you might like’ feature is a recommender system ). Once we can describe the problems properly, we can look for tools, and Python has many! Finally, in the fun part of the presentation, I demoed working code that uses NLTK to build a Twitter sp
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Introduction: What Mugshots Mean For Public Data Posted: October 6, 2013 | Author: Hilary Mason | Filed under: blog | Tags: data , mugshots , privacy | 20 Comments » The New York Times has a story this morning on the growing use of mugshot data for, essentially, extortion . These sites scrape mugshots off of public records databases, use SEO techniques to rank highly in Google searches for people’s names, and then charge those featured in the image to have the pages removed. Many of the people featured were never even convicted of a crime. What the mugshot story demonstrates but never says explicitly is that data is no longer just private or public, but often exists in an in-between state, where the public-ness of the data is a function of how much work is required to find it. Let’s say you’re actually doing a background check on someone you are going on a date with (one of the use cases the operators of these sites claim is common). Before online systems, you c
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Introduction: Where’s the API that can tell me that this photo contains a puppy and a can of Coke? Posted: November 5, 2012 | Author: Hilary Mason | Filed under: blog | Tags: api | 18 Comments » Photo by Ahmad van der Breggen on Flickr. We’ve gotten very good at extracting and disambiguation entities from text data. You can license a commodity system , and there are API and even open source tools that work fairly well. However, a large percentage of content that people share is not primarily text (a back-of-the-envelope guess says around 18%), and we currently have very little automated insight into that content. I know this is a very hard problem, but I’m continuously surprised by how few people seem to be working on it. Any ideas?
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Introduction: Should you attend Hadoop World? Yes. Posted: August 15, 2010 | Author: hilary | Filed under: blog | Tags: conferences , hadoop , hadoopworld , questions | 2 Comments » I received this e-mail via my contact form : I just discovered you via a Google search because I’m highly considering attending this year’s upcoming Hadoop World in NYC. I appreciate your page that you wrote up after attending last year’s event. I’m wondering if you feel that Hadoop has enough momentum and support to be a “here to stay” technology worth investing one’s time and education into, or is it possible it might fade and be deprecated by something else as the need for big data analysis continues to grow? … I’ve had a few similar conversation with people lately, and I thought posting my response might help others making similar decisions. The e-mail is referencing my post from last year’s hadoop world NYC . Thanks for reaching out. There are several questions in your messa
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Introduction: My NYC Python Meetup Presentation: Practical Data Analysis in Python Posted: August 12, 2009 | Author: hilary | Filed under: blog | Tags: data , data analysis , nltk , presentations , python , spam , twitter | Leave a comment » I gave a talk at the NYC Python Meetup on July 29 on Practical Data Analysis in Python . I tend to use my slides for visual representations of the concepts I’m discussing, so there’s a lot of content that was in the presentation that you unfortunately won’t see here. The talk starts with the immense opportunities for knowledge derived from data. I spent some time showing data systems ‘in the wild’ along with the appropriate algorithmic vocabulary (for example, amazon.com ‘s ‘books you might like’ feature is a recommender system ). Once we can describe the problems properly, we can look for tools, and Python has many! Finally, in the fun part of the presentation, I demoed working code that uses NLTK to build a Twitter sp
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Introduction: Why Google Now is Awesome Posted: March 23, 2013 | Author: Hilary Mason | Filed under: blog | Tags: google | 11 Comments » Google Now is an extension to Google’s Android search app that uses all of the data that Google has about you along with what it can guess about your current context to present the information it thinks you need when it thinks you need it. It’ll tell you to leave a bit early to make your next calendar event because of heavy traffic, or that it’s a friend’s birthday, or that there’s a cool cafe nearby where you are. I think it’s amazing. It’s amazing because this is the first Google product that takes ALL OF THE DATA that they have about us and actually makes it useful for us . Not for advertisers. Finally.
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Introduction: Where’s the API that can tell me that this photo contains a puppy and a can of Coke? Posted: November 5, 2012 | Author: Hilary Mason | Filed under: blog | Tags: api | 18 Comments » Photo by Ahmad van der Breggen on Flickr. We’ve gotten very good at extracting and disambiguation entities from text data. You can license a commodity system , and there are API and even open source tools that work fairly well. However, a large percentage of content that people share is not primarily text (a back-of-the-envelope guess says around 18%), and we currently have very little automated insight into that content. I know this is a very hard problem, but I’m continuously surprised by how few people seem to be working on it. Any ideas?
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