Data Science Track Kick-Off Event (2 Talks: Szilard Pafka and Eduardo Arino)

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 ( https://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.