We are starting a Data Science Track that will encompass both Machine Learning as used in business applications and the entire process of data mining (business understanding, data collection, exploratory data analysis, data transformations, feature engineering, modeling, model validation, deployment, communication of results).
A note from Szilard Pafka: Rather than starting a separate Data Science Meetup as initially intended (I also run the LA R and DataVis LA meetups) I joined as a co-organizer and I will be mainly responsible for this track.
This track is rooted in the Panel on Data Science events (2 sessions) at the LA R meetup ( http://www.meetup.com/LA-RUG/events/101484102/ ) and in several application oriented machine learning talks in the past at this (LA Machine Learning) meetup. In the former, we have discussed methods, tools and workflows for data analysis/modeling, skills and organizational issues for successful data science projects, and we catered to both data science/machine learning professionals and business executives interested in extracting value from data.
We'd like to expand on this and bring together machine learning professionals, data scientists, business analysts, data engineers, software developers, data hackers along with startup co-founders, tech, business and analytics executives and anyone interested in extracting knowledge and business value from data. There are several ways to achieve this, for example:
1. Companies doing advanced analytics (their data scientists) can present their craft (case studies). A motivation for companies to do so is to make themselves and their analytic sophistication known in order to attract new talent (recruit) or get feedback on their processes.
2. Data scientists/machine learning practitioners can present (talk) or debate (panel) best practices for extracting knowledge and business value from data (methods, algorithms, software tools, pitfalls, challenges, required skills, organization structures etc).
We are looking for speakers for such future events. If you can present "Data Science @XYZ Co." or you would like to give a talk about your experience, methods, tools or achievements in doing data science, please contact Szilard.
Kick-Off event for the Data Science Track:
For this first such meetup, we'll have two 30-minute talks:
1. Szilard Pafka: 10 Pitfalls in Data Science
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.
- 6:15pm food/drinks and networking
- 7:00pm talks starts promptly
Please arrive by 6:55pm the latest.
Please RSVP as places are limited.
Venue: Cross Campus ( http://www.crosscamp.us/ ) will kindly host this meetup. There is no parking provided. Q ( http://www.qconnects.com/ ) will kindly sponsor/provide the food and drinks.