Gathering, cleaning, manipulating, and assessing data is a complex (and expensive) job – especially if the data takes a wide variety of forms, and comes from many different sources. So why should companies invest in that work?
This talk will review two common use cases for the use of captured metric data: 1) Real-time analysis, visualization, and quality assurance, and 2) Ad-hoc analysis. Once metric data is generated, to support the use cases mentioned above it must be ingested properly using a robust and fault-tolerant streaming framework. The most common open source … Read More
In this episode, host Ken Rimple talks to Chris Baglieri of BlackFynn on the company’s work in health research-related data science. He discusses how he and his team aids researchers who are attacking Parkinson’s disease and other disorders. They’ve been doing the deep genetic research that has benefitted cancer research over the past two decades. … Read More
Disclaimer: Some basic exposure to machine learning is assumed. Neural nets are on the rise, now that computing power and parallel data processing capabilities have reached the levels that allow them to shine. Recurrent neural nets, the more sophisticated kind that possess time dynamics, have achieved spectacular results in certain areas. Overfitting, however, has … Read More
I recently attended the O’Reilly AI Conference in New York where artificial intelligence practitioners showcased the impressive strides they’ve made so far in using AI for real-world applications
Predictive modeling is one of the figureheads of big data. Machine Learning Theory asserts that the more data the better, and empirical observations suggest that the more granular data, the better the performance (provided you have modern algorithms and big data) but the paradox of predictive modeling is that when you need models the most, even all the data is not enough.
Tracey Welson-Rossman talks to Anita Garamella Andrews, VP of Client Analytics Services at R.J. Metrics, about analytics and actionable data.
Sujan Kapadia writes: “This year I’ve started going to the DataPhilly meetups, and I think I’m hooked. The bottom line is DataPhilly talks are very intriguing, expose you to topics you don’t encounter everyday, and give you the chance to meet “non-traditional” developers (scientists and statisticians), whose ranks are rapidly growing.”
We are holding an all-day event on October 30th, downtown in the Philadelphia Cira Centre, that shines a light on large-scale data processing and application management. In this article I’m going to explain a bit about the event’s goals, and some information on the speakers and talks we’ve been lining up.