In this talk I will discuss 10 common pitfalls in doing data analysis, predictive modeling and developing analytical systems in a business environment. Some of these issues are analytical, some are technical while some are business/organizational in nature, so the talk will cover a variety of topics (at various levels from higher-level to more technical) and it should be relevant to a wide range of people interested in data science (data scientists, tech professionals, business executives).
Bio: Szilard Pafka is the Chief Data Scientist at a credit card processor in Santa Monica and a leader of the LA data community. He combines a PhD and more than 15 years of practical experience in performing data analysis and developing analytical systems focused on achieving business goals. He is the founder and organizer of the LA R and DataVis LA meetups and became recently a co-organizer of the Machine Learning meetup. More detailed bio here: http://www.linkedin.com/in/szilard
2. Eduardo Arino de la Rubia: Bootstrapping a Data Science Practice at your Company and in your Career
Being a data scientist can require a PhD, an obsession with matrix notation, and a love of stochasticity. I have none of those. Fortunately, data science can also involve a love of tinkering, data munging, and providing glue between the components brilliant people have built for us. As long as you’re hungry to learn and humble enough to keep asking questions, data science is within your grasp. I’ll share my experiences that led to becoming my company’s de-facto data scientist, how I transitioned to this new kind of hybrid role, and showcase the types of “low-hanging fruit” I was able to address – all while looking great doing it!
Bio: Eduardo Arino de la Rubia is a husband, father, and genuinely fortunate fellow. He started programming when he was 4 years old on a Sinclair ZX Spectrum, and has spent the last 31 years questioning that decision. An exposure to Genetic Algorithms and evolutionary computing taught him that sometimes an indirect approach has real benefits, and the first time he was exposed to a skiplist he realized that messy stochastic approaches often times outperform the best intentions. He has a BS in CS and recently completed General Assembly’s Data Science Program.