Your title should be both vague and specific.
First, vague. You generally have to commit to give a talk months in advance of the actual event. You do not, however, generally have a talk written several months ahead of the actual event. You may also have a particular talk accepted, and then arrive at the conference and realize that what you had planned isn’t ideal for that audience. A vague title offers you a lot of flexibility in altering the content of your talk as conditions change without betraying the expectations of the audience based on the materials published earlier.
And then, specific. If your title is too vague (“Stuff and Junk”) people won’t be excited for your talk, and you’ll lack an audience entirely or won’t make it through the CFP process at all. Be specific about the frame of the talk, but leave the details vague.
For example, I recently gave a talk called “Human Behavior and the Social Web”. The title gives you a good idea what the talk will be about, but doesn’t commit me to sticking to any particular set of stories or material.
A particularly excellent example of this is Paul Graham’s PyCon 2012 keynote titled “Frighteningly Ambitious Startup Ideas” (which was also a really fun talk). That title gives you a specific frame to get very excited about, while leaving him with complete flexibility to alter the content up until the moment he got on stage.
This article is part of my series of speaking hacks for introverts and nerds. Read about the motivation here.
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!
The first Strata Conference in New York just wound up. It was a five day expo of business, data, and tech, and brought a ton of great people in the data community to New York.
Thanks so much to Edd and Alistair and everyone whose hard work made this possible!
My talk, Short URLs, Big Data: Learning in Realtime is already online:
And the slides are up on Slideshare:
The video from my keynote at Strange Loop 2010 is up!
You can watch the video here: Machine Learning: A Love Story
The original abstract:
Machine learning has come a long way in recent years — from a long-marginalized field so old it still has the word “machine” in the name, to the last, best hope for making sense of our massive flows of data.
The art of ‘data science’ is asking the right questions; the answers are generally trivial or impossible. This talk will focus more on questions than on answers. I’ll give a brief history of the field with a focus on the fundamental math and algorithmic tools that we use to address these kinds of problems, then walk through several descriptive and predictive scenarios.
Finally, I’ll show one example system using bit.ly data in-depth, from the backend infrastructure through the algorithms and data processing layer to show a functioning product.
Attendees should expect to hear some good stories of data gone right and data gone awry, and walk away with a few new clever tricks.
The presentation was calibrated for the audience in the room, but I’ll be happy to answer any questions in the comments below!
The Ignite events are a fun blend of performance, technology, and speaking skill. Each presenter gives a five minute talk with twenty slides that auto-advance after 15 seconds.
The title of my talk is a classic geek reference (you can get the t-shirt). I’m very interested in developing automated techniques for handling the massive and growing amounts of information that we all have to deal with. I started with e-mail and twitter, both of which are easy to access programmatically (via IMAP and the Twitter API).
In the talk, I went through several of the simple and successful e-mail management scripts that I’ve developed.
I decided to talk about this project because I’m not sure where this should go next, but I got some great feedback and I’m looking forward to future work on the project!
The slides are below, and the full talk will be online soon.
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 spam filter with 90% accuracy*.
Please let me know if you have questions or comments.
* I’ll post the code and training data shortly
I gave a talk at BarCampNYC4 on Saturday on common data problems and a very light overview of algorithms that address them.
I delivered the majority of the content verbally, by talking through examples of problems and how to solve them, so there’s no guarantee that these slides will make sense, but they might be funny!
Sanford took some excellent notes during the presentation.
The discussion was so lively and engaging that I’m planning to expand on this content — I really welcome your suggestions and comments!