Tuesday, September 30, 2014 at 3:46PM 
If you an experienced data miner looking for a new challenge or a recent graduate just looking to enter the field, being prepared for interviews is crucial. In my years of leading a data mining company, I have done my share of interviewing candidates for open positions. I thought you might like to hear about my approach to interviewing and hiring.
Thursday, November 8, 2012 at 8:48PM 
When creating a predictive model, data miners need to “tune” it to our client's needs. We need to strike the right balance for them between missed opportunities and false alarms. For some clients, the model's cut-off point between 'promising' and 'unpromising' must be set lower to avoid missed opportunities. For others it needs to be set higher to avoid false alarms. Since no predictive model can be perfect, we need to tweak it to make the "right kind of mistakes." Read the full article.
Tuesday, October 2, 2012 at 6:27PM Discovery Corps president Tim Graettinger is a co-presenter of a free webinar on Data Mining offered regularly (about once a month). These 1-hour interactive events are offered by The Modeling Agency, one of Discovery Corps’ marketing partners. The next webinar will be Wednesday, December 12th at 4:00 pm (U.S. Eastern). The webinar, titled “Data Mining: Failure to Launch,” provides a brief introduction to data mining, as well as a Question & Answer session. It’s an especially good opportunity for businesses who have been contemplating a data mining project to ask questions of a data mining pro. Find out more about it here. Then join Tim and ask the questions that have been on your mind!
Monday, July 16, 2012 at 10:21PM 
Where in the data mining process do data scientists add the most value to our customers? I believe we do so by framing the problem well -- clearly, explicitly defining what the problem is and is not. In Part 2 of this series, we look at these three key questions:
Monday, June 18, 2012 at 10:09PM 
For all the strides that data mining tools have made, using them well still requires hard work and critical thought. This article is one in a series where we’re reviewing what it takes to be successful with data mining, what the common pitfalls are, how to avoid or remedy problems, and how to interpret results. This time we adresses a real workhorse for data mining and analysis, the histogram. Among the histograms encountered most frequently in practice are the following: “money”, “count”, and “outlier”. We will look at each one of them in turn. Read the full article