|Bio||Title - Description|
|David Weisman, Ph.D. is an expert in data science, with over 30 years of accomplishment in IT consulting and computer science. His career began in compiler design, and in 1980, David co-founded Lydon LLP, a successful consulting firm. For over 25 years, Lydon developed financial trading systems, middleware architecture, compiler tools, and secure distributed systems. Lydon's clients included EMC, ITT, Open Software Foundation, State Street Bank, Fidelity Investments, Harvard Management Company, Iron Mountain, Burlington Northern Railroad, British Telecom, Putnam Investments, Trident Capital, BankBoston Global Capital Markets, Osborn Capital, DataMedic, Clinical Information Advantages, Liant, Stratus Computer, Individual.com, Interwoven/NeonYoyo, and Sun Microsystems.|
From 2005-2011, David paused his consulting career to earn a Ph.D. in Molecular Biology, focused at the intersection of big data mining, applied mathematics, and biology. His research includes applying machine learning to develop environmental biomarkers; analyzing microbial metagenomic DNA sequence data; developing algorithms that detect alterations in cancer genomes; analyzing gene expression and epigenetic changes indicating emotional stress in mammalian systems, and elucidating microbial responses to nanomaterials. David's academic accomplishments won awards of Outstanding Achievement in Molecular, Cellular and Organismal Biology, as well as Outstanding Teaching.
After completing graduate school, David resumed consulting and is now providing expertise in data science. His current projects include text and social media data mining, churn prediction, customer segmentation, and enterprise-level strategy for predictive analytics. His clients include consumer product companies, large consulting firms, technology platform providers, financial service firms, and biotechnology companies. In addition, David is in high demand as a public speaker, and frequently presents talks and seminars on data science.
|Introduction to the Data Science Method: Think About Data|
You lead a data science team at a large hospital system, and you built a model that predicts which patients will incur high medical costs. The CEO is using your predictions to aggressively limit spending on these patients, and just held a big press conference to publicize your cost-saving breakthrough. Any concerns?
At every stage of analysis, it's essential to really think about the data: where it came from, how it's biased, how it was sampled, and how it can be extremely misleading. In this beginner-level workshop, we'll dissect and analyze real-world data, and you'll leave with a much larger awareness of data itself. This awareness could save your CEO some major embarrassment.
|Data Science & Engineering