The Data Science Revolution in Industry
The hype around “the thinking sciences” — Artificial Intelligence, Machine Learning, and Data Science — is enormous, so it’s tempting to be skeptical of the gains claimed. Still, most of the results are real. The capabilities of Data Science, where models are inductively built from real history, have been growing steadily. I will reveal as examples five of our most interesting fielded solutions from the last two decades, from the diverse worlds of investment timing, credit scoring, drug discovery, medical diagnosis, and gas exploration.
Presenter: John Elder, Chair & Founder, Elder Research, Inc.
Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL
Networks are a data structure commonly found in any social media service that allows populations to author collections of connections. The Social Media Research Foundation, formed in 2010 to develop open tools and open data sets, and to foster open scholarship related to social media, has released the NodeXL project, a spreadsheet add-in that supports “network overview discovery and exploration.” The tool fits inside your existing copy of Excel in Office 2007, 2010 and 2013, and makes creating a social network map similar to the process of making a pie chart. Recent research created by applying the tool to a range of social media networks has already revealed the variations in network structures present in online social spaces. A review of the tool and images of Twitter, flickr, YouTube, Facebook and email networks will be presented.
Presenter: Dr. Marc Smith, Founder, NodeXL
From Statistics to Data Science Startup: Transformation Within a Large Research Organization
We will discuss building a thriving Center for Data Science within a large and well-respected non-profit research institute; our mission, vision and principles; and the type of work we do. We are a team of 21 data scientists, statisticians, software developers and artists (yes, artists) at RTI International, where our overall mission is to improve the human condition, and our Center’s mission is “data science for social good.” What is it like building and leading such an organization from scratch after 26 years as a statistician at RTI? We’ll cover several important considerations, some of which we committed to early on, some of which evolved (and continue to evolve) through practice and experimentation: choosing a leader, team composition, continuous recruitment and retention efforts, the necessity of technical as well as domain and soft skills, the idea of team vs. group culture, elements of our philosophy—continuous innovation, collaboration, rapid prototyping, human-centered design, an open source mindset, and entrepreneurship—that drive success in collaborating with domain researchers across RTI, fostering staff engagement, and creating a continuous learning environment in a fast-moving field. Finally, we’ll cover some of our most impactful projects and some of our best adventures to date, those solving important national problems, improving our local communities, and transforming research.
Presenter: Gayle S. Bieler, Director, Center for Data Science, RTI International
Analytics at GM
General Motors is a $150Bn multinational corporation with operations spread across six continents and 140 countries, producing 30,000 vehicles a day in 30 countries. The opportunity for applying advanced analytics in a global operation as complex as this is quite significant. While General Motors has benefited from advanced analytics for several years in select areas, it has more recently invested heavily in the broad use of advanced analytic methods across the enterprise. This has included the formation of a central team of senior data scientists and data engineers, the buildout of state of the art technology, and consolidation and integration of company data on a massive scale. Advanced analytic methods have been applied across the business lifecycle, from early market forecasting and strategic planning through in-market pricing and supply chain optimization. These models have drawn from a combination of traditional and big data analytic disciplines, including econometrics, text analytics, deep learning and optimization.
Presenter: Tom Capotosto, Director, Advanced Analytics, General Motors
Big Data and Big Analytics – Opportunities for Inter-disciplinary Innovation
Data volumes continue to increase at a rapid pace along with a need to solve complex business problems based on insight gained from hybrid sources of data. At the same time, computing power and access to multi-processor hardware configurations enables us to solve increasingly complex problems which were intractable before. Often, solutions to the most challenging problems require the invention and combination of many new techniques and algorithms which span multiple analytical disciplines such as forecasting, estimation, predictive modeling, data mining and optimization.
This presentation will provide several examples that describe some of these innovations in various industries as well as discuss trends and upcoming challenges for future research.
Presenter: Dr. Radhika Kulkarni, VP, Advanced Analytics R&D, SAS Institute Inc., & Udo Sglavo, Senior Director R&D, SAS
Automating Machine Learning: A practical guide to AI adoption
The accessibility of machine learning techniques has never been greater. But there are significant adoption challenges stemming from the need for iterative approaches and a shortage of people with the right mix of skills and domain knowledge. Through specific use cases, this session will explore how advanced techniques can be automated to unlock machine learning for a broad set of users with diverse technical backgrounds.
Presenter: Jeff Holoman, Director of Sales for the Southeast, DataRobot
Transforming Your Business with AI
AI discussions are dominating the world of business. We hear stories almost daily about jobs being replaced with human-like computers and algorithms, and doomsday predictions about computers taking over the world. Is AI right for your organization? In this discussion, we will define what is and is not AI, how it differs from analytics of the past, provide some current business problems currently being solved using AI, and, most importantly, give you guidance and key questions to ask to determine if your organization is ready to invest in this exciting area.
Presenter: Scott Langfeldt, Founder and CEO, Apex Data Science