A Data Science Central Community
Oftentimes a distressed firm considering a uplift plan will not be successful without some type of financial restructuring, and sometimes the best way to recover a distressed firm’s existing value is through liquidation. Clearly, many analytics professionals are analytics-driven experts that must be capable of determining the financial state of a firm. If the core business has been irreparably damaged and the future outlook is bleak then the analytics will confirm. Under the worst case,…Continue
Regardless of the real-world experience as executives, attorneys, financial experts and CPAs, providing evidence without analytics diminish your court room success rate. Eventually, professionals that are uniquely qualified to provide litigation support services to law firms are needed. The shortage of professionals that work side-by-side with ligitation staff including attorneys to find winning solutions and offer the following services ground by analytics and intelligence is surprising.…Continue
Added by Patrick C. Walker, MPA on August 31, 2013 at 3:01pm — No Comments
The leadership analytics style needed for a distressed company is very different from that needed for a healthy firm. The focus for a healthy firm should be on achieving the firm’s objectives while the focus for a distressed company is recovery, timely action, and problem resolution. Senior management’s authority in a healthy firm is one of delegation, while in a crisis situation the executives need to get directly involved. And the decision-making skills of a healthy company executive are…Continue
Added by Patrick C. Walker, MPA on August 31, 2013 at 2:35pm — No Comments
A growing gap between those enterprises that, see the value of business analytics and are transforming themselves to take advantage of these new found opportunities, and those that have yet to embrace them. Businesses today have a plethora of data available from an increasing number of sources. While some appreciate the potential benefits others face difficulties using data beyond hindsight, to actionable insights or further - foresight. Read these books to learn more!…Continue
Added by Patrick C. Walker, MPA on August 31, 2013 at 2:30pm — No Comments
IBM Wilhelmina (willing to protect) is a challenge to IBMers from Street Learnsters - individuals committed to improving learning capacity using all methodologies regardless of prior shortcomings. The challenge is not to create Wilhelmina but to continue the efforts that emphasize the presence of a attitude and commitment willing to protect everyone. Naturally, a bold declaration will have to compliment what IBM currently do today and what the Smarter Planet initiative will require tomorrow.…Continue
Added by Patrick C. Walker, MPA on August 31, 2013 at 2:07pm — No Comments
These very curious celestial bodies are in-between a moon and an asteroid. They are called trojans: they follow the same orbit as Earth (around the sun), their path is locked to Earth and they won't collide with us. Gravitation law dictates their choreographic orbits.
In the July 28 2011 issue of the journal …Continue
Added by Vincent Granville on August 30, 2013 at 12:30pm — No Comments
Added by Patrick C. Walker, MPA on August 29, 2013 at 12:42pm — No Comments
Data is bits and bytes and visualization has the power to tell the story in multiple forms. Today I wish to share two different visualization for the same dataset.
Here is the link to the dataset and the dashboard as shown below
And here is…Continue
I originally published this article in Analytics-Magazine.org. The article relates to my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or…Continue
Data analysis echo system has grown all the way from SQL's to NoSQL and from Excel analysis to Visualization. Today, we are in scarceness of the resources to process ALL (You better understand what i mean by ALL) kind of data that is coming to enterprise. Data goes through profiling, formatting, munging or cleansing, pruning, transformation steps to analytics and predictive modeling. Interestingly, there is no one tool proved to be an effective solution to run…Continue
There have been 5 summers since former Wired Editor-in-Chief Chris Anderson published an article about Big Data that concluded with the following statement: “Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all.” Big Data is not only changing science–it’s changing the way businesses interact with and gain insight…Continue
Added by Radhika Subramanian on August 26, 2013 at 12:35pm — No Comments
If you're a modeler, you might say, “who the heck is this guy telling me that my precious thing is useless?” Wait a minute. I will explain later.
Let me tell you a story. Once upon a time, I was preaching in front of senior management on how we could get more money. Using predictive model, it was proven that business could get cleaner leads, the right customer with higher likelihood to take our product. On paper, we could increase the revenue. It turns out, the customer response went…Continue
And the rise of the data scientist. These pictures speak better than words. They represent keyword popularity according to Google. These numbers and charts are available on Google.…Continue
Since February's launch of my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, I have participated in a number of video interviews that explore the topic and field of predictive analytics. Here is a sampling:
Bloomberg TV – Predictive Analytics in Four Minutes:
Added by Eric Siegel on August 21, 2013 at 1:16pm — No Comments
Whether you own a sports team, run a ski resort, or sell school supplies, you’ve likely come across some seasonality in your sales and revenue. Indeed, even seemingly noncyclical businesses, like fast food or clothing, experience some sort of seasonality.
Seasonal sales are risky because, in the most extreme cases, you have only a few days to make your sales numbers. Imagine you’re a retailer preparing for Christmas — success or failure in mid-December might mean success or failure…Continue
Added by Wojciech Gryc on August 20, 2013 at 12:30pm — No Comments
We've been working hard to build a connection between the many customer data APIs we connect to, and a server-based instance of R. We've made some great headway today by building customer-oriented models that are generalized to any data set we build on our platform! Read the tutorial, and let us know what you think.
Added by Wojciech Gryc on August 19, 2013 at 8:45pm — No Comments
Data projects are becoming a necessity across industries, and they’re intimidating analysts and managers alike. The data is there, lurking in the back of workers’ minds and invading everyone’s thoughts, planting seeds of uncertainty into decisions because companies just don’t know what they don’t know.
The development of a successful data analytics project doesn’t have to be so imposing that it stalls the work that needs to be done. Developing an outcome-driven analytics…Continue
Added by Radhika Subramanian on August 19, 2013 at 2:30pm — No Comments
Posted in the Washington Post.
1. A political map of the world, circa 200 A.D.…Continue
Added by Vincent Granville on August 19, 2013 at 12:30pm — No Comments
A fundamental question faced by business analytics professionals and data scientists is whether they have a working correlative and causal explanatory model related to the phenomenon they are observing, be it related to reducing manufacturing error rates, determining the cause of customer abandonment, reducing fraud, targeting marketing, realizing logistics efficiencies, etc. This is known as an experimental model in science or a conceptual model in broader research venues (i.e. social…Continue
Added by Scott Mongeau on August 19, 2013 at 12:55am — No Comments
Improperly ‘conflating causation with correlation’ is a central but often overlooked danger in business analysis and data science initiatives. Especially with ‘big data’ sets, analysis will often reveal patterns that suggest a causal element which is only co-occurring phenomenon, or worse, ‘phantom phenomenon’ (i.e. coincidence or a happenstance of a limited dataset).
Some practical examples concerning mistaking correlation for causation: a recent letter to the editor in the…Continue
Added by Scott Mongeau on August 19, 2013 at 12:53am — No Comments