Filed under: #mktgcloud, Algorithms, Data, Datarati, Sydney Data Miners | Tags: Data Mining
Filed under: #mktgcloud, Algorithms, Analytics, Data, Datarati | Tags: Data Mining, Kaggle
Starting in early April, Kaggle will be hosting the world’s biggest-ever data mining competition – the$3m Heritage Health Prize. The Heritage Provider Network (HPN) is a network of affiliated medical groups and physicians that is dedicated to helping solve a critical issue facing the United States: how to improve the quality of healthcare while at the same time decreasing the cost of providing that care.
Kaggle is immensely proud to be providing the platform for this competition, which we hope will result in those most in need getting faster and cheaper access to healthcare.
If you haven’t yet tried entering a Kaggle competition, we strongly suggest you start getting involved, to start sharpening your skills and building your network.
There are four interesting and varied competitions on the site right now and by getting involved, you’ll learn a lot about how to compete effectively and may make some great relationships with other competitors, who you could team up with in the $3m prize.
Filed under: #mktgcloud, Algorithms, Datarati, Predicitive Modelling | Tags: Data Mining, Predictive Analytics, Video
In this session Neil explores the approach to insight generation through data mining and predictive analytical technologies.
Using real world case studies he covers the ins and outs of data mining analytics on digital data, which types of techniques can be used to solve which kinds of problems and some of the challenges that you will inevitable face along the way. Discover what your data can tell you if you ask it the right questions.
Data Mining Process and Predictive Analytics
In Part I (out of three), Neil discusses the difficulty of extracting signals from all the noise present in the overwhelming quantity of data we deal with nowadays. While we now have tools for free and the cost of collecting data is diminishing, Neil questions: “what do we do with all this data?”
He introduces other tools that can help us to address some of these challenges:
- Data Mining is about discovering things we don’t already know. It is about uncovering patterns and relationships in data that we may not have already thought about.
- Predictive Analytics is using that understanding to think about what might happen in the future; it is about applying those historical patterns to predict those future outcomes.
Neil goes over the Data Mining Process and the steps needed in order to implement it and, more importantly, to get insights out of it:
- Business Understanding: what problem are we trying to solve? What is the business trying to achieve?
- Data Understanding: do we have the data to be able to answer this questions? If not, what is the cost of acquiring that additional information?
- Data Preparation: all data is dirty and needs to be cleaned and transformed. This is the heavy lifting stage.
- Analysis & Modeling: the tools must be chosen based on what the business is trying to understand and the data available.
- Evaluate Outcomes: how well does the model actually works from a statistical point of view (significance) and from a business point of view (actionability)?
- Deployment: driving the insight into the business.
Neil Mason joined Foviance as part of an acquisition of Applied Insights whom he was director and co-founder. With 25 years of in-depth industry experience in marketing analytics and strategy, Neil leads Foviance’s analytical consulting practice.This delivers an enhanced digital marketing analytics capability to both Foviance’s and Applied Insights existing and future clients.
Neil is one of the world’s leading analytics guru’s and he has a big reputation. He holds an MBA from Kingston Business School, a Diploma in Business and Economic Forecasting and currently serves on the Board of Directors of the Web Analytics Association, the global industry body for digital analytics professionals.
Filed under: Actionable Insights, Analytics, Datarati, Mobile | Tags: Data Mining, Flirtexting
OMG, LOL, BRB, J/K…
On Friday night, I had the pleasure of meeting Debra Goldstein & Olivia Baniuszewicz, the two girls who are in Sydney from New York promoting their book called Flirtexting.
I have a feeling our mobile phone providers could be using this Flirtexting data to generate some pretty interesting insights and target us with relevant advertising.
Our next event is being held this week on Wednesday 23rd at 6PM at the City Hotel.
For event details and FREE registration: www.meetup.com/datarati
Filed under: Algorithms, Analytics, Datarati, Predicitive Modelling | Tags: Data Mining, Predictive Analytics, Visa
If you ever doubted the power of the credit card companies, consider this: Visa, the world’s largest credit card network, can predict how likely you are to get a divorce.
There’s no consumer-protection legislation for that.
Why would Visa care that your marriage is on the rocks? Yale Law School Professor Ian Ayres, who included the Visa example in his book Super Crunchers, says “credit card companies don’t really care about divorce in and of itself—they care whether you’re going to pay your card off.”
And because people who are going through a divorce are more likely to miss payments, your domestic troubles are of great interest to a company that thrives on risk management.
Filed under: #mktgcloud, Analytics, Datarati, Privacy | Tags: Data Mining, Facebook
Data mining is now one of the biggest minefields facing the young.
Party antics and examples of extreme behaviour posted for fun now on Facebook and other social networking sites are set to become ghosts that haunt individuals when they try to get credit, homes or jobs as adults.
That’s because lenders, employers and landlords are increasingly using complex data mining tools to capture all the publicly posted data we supply to Facebook, Twitter, Flickr and any other social media network or blog to build data-rich profiles of our privates lives, internet privacy experts say.
Data mining is a great career for people who would enjoy using statistics to unearth patterns in data, using ever more powerful software. Opportunities are particularly good if you also have business sense and the ability to tease out the information that bosses really want to know.
Filed under: Data, Visualisation | Tags: Data Mining, Open Source, R, Robert Gentleman
R is also the name of a popular programming language used by a growing number of data analysts inside corporations and academia. It is becoming their lingua franca partly because data mining has entered a golden age, whether being used to set ad prices, find new drugs more quickly or fine-tune financial models. Companies as diverse as Google, Pfizer, Merck, Bank of America, the InterContinental Hotels Group and Shell use it.
The most extensive government report to date on whether terrorists can be identified through data mining has yielded an important conclusion: It doesn’t really work.
A National Research Council report, years in the making and scheduled to be released Tuesday, concludes that automated identification of terrorists through data mining or any other mechanism “is neither feasible as an objective nor desirable as a goal of technology development efforts.” Inevitable false positives will result in “ordinary, law-abiding citizens and businesses” being incorrectly flagged as suspects.
The whopping 352-page report, called “Protecting Individual Privacy in the Struggle Against Terrorists,” amounts to at least a partial repudiation of the Defense Department’s controversial data-mining program called Total Information Awareness, which was limited by Congress in 2003.