Most of the sessions will be picked based on pitches in our opening session. The method is simple: anyone who wants to present a session, or lead a discussion, can propose it. We'll fill out the schedule based on which sessions people want to attend (if you haven't been to an unconference before, this works better than you might think). We'll have a few invited speakers to cover some important topics, but most of our day will be based on sharing what we know with people who want to learn.
If you plan to pitch a session, please add it to the list below (you'll need to create a free Wikidot account to make changes to the wiki). Remember to register, too. If you need ideas, you might find inspiration on the session ideas page or in last year's proposals, but you're not limited by our imagination. You can pitch anything you want at AnalyticsCamp, and everyone there will decide whether they want that session to happen.
Include session title, description and your name. Link to a reference or blog post if that helps. Adding your Twitter handle would be a nice touch, too (Session leaders list on Twitter).
- R: The Good and The Bad The free and open-source R language and environment is rapidly becoming the lingua franca of statistical computing and the platform of choice for data analysts and research scientists. We'll talk about what's good and what's bad about R. We will discuss implications, problems, and new opportunities surrounding the increasing popularity of R. Discussion will cover topics of interest to current or aspiring R users within the academic, business, and nonprofit communities. Ian Cook, TIBCO (@ianmcook) (Slideshare)
- Edward Tufte and Information Design Strategies for the Web "Confusion and clutter are failures of design, not attributes of information. And so the point is to find design strategies that reveal detail and complexity rather than to fault the data for an excess of complication." —Edward Tufte. In this presentation, we first present an overview of the work of information design expert Edward Tufte and then discuss concrete applications and examples for applying his principles of analytical design to the Web. Selected themes include: contrast and meaning in design, avoiding “chartjunk” and “computer administrative debris”, clarity and clutter, the value of aesthetics, and simplicity vs. minimalism. Nathan Huening (@sprockethouse)
- Breaking up isn't so hard to do. Providing business analytics capabilities to all the various user communities in your organization probably is a KEY requirement of your business intelligence implementation. Session will discuss how to break up the different user communities into logical groups and ensure each gets what they need. Angela Hall (@angelahall1)
- The Social Media Marketing Tool Stack. Social media marketing is hard…and the flood of social media marketing tools doesn't really make it much easier. This session provides a framework for understanding social media marketing tool categories, applying them to your business, and collecting the right data to make decisions. Eric Boggs, Argyle Social (@ericboggs)
- Web-based Visualization using Protovis and/or HTML5 Canvas Prototyping visualizations for the web has never been easier! We'll talk about how to prepare the data and how to create interactive visualizations that work in all modern web browsers. Depending on interest, we can cover protovis or HTML5 Canvas, or perhaps even both (though we should probably stick to one). Robert Kosara (@eagereyes)
- Empathetic Optimization: How Combining Emotional Intelligence with Actionable Data Can Help Achieve Unprecedented Profit To an outsider, analytics can seem like a bunch of cold data that has nothing to do with the human beings who generated the data trail. But the best analysis balances subjective and objective reasoning, looking for emotional and motivational cues and finding data-driven means of validating them. With this approach, analysts can find hidden insights and develop the basis for optimization campaigns that can vastly improve the user experience and take profit potential to the next level. Kate O'Neill, [meta]marketer (@kateo)
- Small group discussion: getting started with data mining the programmable web Are you experienced with Twitter, LinkedIn, del.icio.us, etc., APIs? Or perhaps you're an experienced data miner? Interested to link the two to create interesting mashups such as recommendation engines, or even predict real world events (stock movements, election outcomes, box office returns, etc.)? Or, have you already done so? I have experimented with grabbing Twitter data to create a basic friend recommendation engine using SAS, but am far from being an "expert" - I can share my experience and would love to learn from you about any of the above. So let's talk and learn from each other's expertise or experience, whatever the level, and maybe get an idea for someone's next project. Small group discussion. I-kong Fu, SAS (@ikongsgf)
- Have a Job/Need a Job A good idea from BarCampRDU: we'll open the mike during lunch for anyone who's looking for a job or someone to fill one.
- Great Analytics Come from Great Planning With all the technology available sometimes we forget to adequately plan what we will measure when the project is complete. If we do not properly capture the analytics in the early stages it can cause problems. This session will discuss strategies and methods to accurately define metrics for projects beyond the project plan at the early stages. It will also discuss strategies for defining hard to measure business processes and activities. Brian McDonald, Square Jaw Media (@bmcd67)
- Text Analytics Made Easy, WTF? I know I hear you now If text analytics is so wonderful why then are people saying "Sentiment Analysis is worse than a coin toss." Yeah, Eric Peterson said "Web Analytics is hard", he never did text analytics. I'll debunk myths and demonstrate ways to make text analytics easier and make it work for you. Richard Foley, SAS (@richardfoley)
- Why your Sentiment Analysis is Wrong Sentiment Analysis, its accuracy, and its value are hotly debated topics right now. Taking a statistical and machine learning approach, I'll provide some theory and actual results that show why sentiment analysis is so difficult and how it can be done better. T.R. Fitz-Gibbon, Chief Scientist, Networked Insights (@trfitzgibbon)
- Predictive Text Analytics Ok - you've done some cool Sentiment Text Analytics on what your customers are talking about … now what ?. I'll discuss and demonstrate the value in layering Predictive Analytic techniques over a Text Analytics platform to 1) reveal facets of your business that attract/repels customers, and 2) create capability to anticipate shifting customer loyalty. Thomas Mathew, Predictive Analytics Manager, Clarabridge
- Ask the Recruiters You've heard that analytics is a hot topic and companies are hiring, but you want details. What are companies really looking for? Bring your questions to this session, as two retained-search recruiters share what they're seeing in the market. Bill Goodwin and Nadine Rubin, AdamBryce
- Marketing to the Customer Life Cycle Customer dynamics have been permanently altered by the Internet and social media. The removal of geographical and logistic barriers created new customer types. Companies are discovering that up to 40% of new customers acquired are hit-&-runners. These people have a limited lifespan. They make one to three purchases, and then disappear. Marketing dollars are wasted when they placed in a marketing cycle that doesn’t recognize their limited participation. Creating a marketing strategy based on individual customer types and their position in the life cycle increases retention, reduces costs, and improves return on investment. In this session, Debra Ellis will focus on how to identify and market to Web 2.0 customers. You can read more in the Customer Life Cycle series at the Multichannel Magic Blog. Debra Ellis, President, Wilson & Ellis Consulting (@wilsonellis)