The Data Science Conference 2015 Highlights

Here are the highlights from The Data Science Conference 2015, Nov 12-13 at University of Chicago. A two-day conference on Data Science, big data, machine learning, artificial intelligence & predictive modeling discussions -"for professionals" by professionals.

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Introduction: The Data Science conference featured 23 speakers and brought together 107 professionals working in the field of data science, data mining, big data, machine learning, artificial intelligence and predictive analytics. The tone of the main conference was set during the opening remarks by Michael Tsiappoutas, Ph.D., Conference Chair who introduced the conference organizing committee & volunteers.

Conference Overview:
The fast-paced 20-30 minute presentations kept the audience at the edge of their seats & interactive questions made them part of the show. The conference format was well-balanced with breakout sessions seen with professionals brewing up new collaborations.
They could freely exchange experiences & ideas with fellow practitioners without being prospected by any attendees. The conference kept up with it's theme: vendor-free, recruiter-free and sponsor-free.

The Conference from Participant's eye: Most attendees described the event as “friendly" and “well-paced". Some comments collected from attendees & speakers and the Tweets about the conference:
  • The Data Science Conference℠ aims to fill a void by putting "science" back into Data Science conferences, Nov. 12-13 - Omar Alonso @elunca
  • Looks interesting. Vendor-free, sponsor-free, and recruiter-free data science event in Chicago: -Rever.Eu @Data_lifecycle

"I could relate data science examples from Academia with most of the high level practitioner talks, which for me is a big treat! " -Jean-François Plante, Professor Statistics, HEC Montreal, Attendee

"Lack of people trying to sell meant more time for me to actually have conversations about data science" -Dana Ferguson, Expert Data Scientist, Allstate, Speaker

Conference Topics: The talks focused on a wide range of topics including data science education, machine learning & Artificial Intelligence applications, risk management, text mining, agile methodologies, gaming, applications of data science in the fields of marketing, social media, insurance, organizational efficiency etc, just to name a few.

A few presentation insights:
  • Nitish Kanabar gave a peak into 11 MOOCs chosen based on O’reilly data science salaries & skill requirement survey 2015. Some of the MOOCs mentioned by him are Machine Learning by Andrew Ng, Introduction to Statistical Learning by Trevor Hastie and Rob Tibshirani, Analytics edge by ‎Dimitris Bertsimas, Mining massive datasets, R programming by Roger Peng, Learn Python the Hard way by Zed Shaw among others.
  • Brandon Rohrer’s title captioned “The other stuff” took the audience to an advanced thought process when he talked about feature engineering concepts in data science and introduced methods in handling missing values.
  • Nick Kadochnikov presented an insightful talk on impact of fraud, abuse & error on today’s businesses and highlighted the fact that organizations lose 5% of revenues to fraud. His analysis made the audience ponder about how data science can be used in various domains to address & prevent fraud.
  • Mike Jaron questioned the current system of doing mere sentiment analysis. He presented a step by step engaging analysis on how the internet itself is almost a survey tool if actionable insights can be derived.
  • Talk about gaming? Scott Burger’s analysis on PC gaming user based reviews based on aggregate emotional analysis took the audience minds to data science in the video games world.
  • Shavar Beckford's presentation on agile methodologies clubbed with data science customizations was useful for professionals handling complex projects.
  • Examples of Applications in Artificial Intelligence was presented by Don Dini! He started off with a simple engaging example of sequence of actions, provided clear distinctions between Machine Learning(focused on Learning) and Artificial Intelligence (focused on Acting) and then took the audience minds to concepts of Decision theory, Utility theory, Markov decision processes etc.
  • Bamshad Mobasher captured the first moments of his talk with a visual on “What happens in an internet minute?”. He later mainly explained the challenges in data distillation & integration and focused on Recommender systems.
  • Ling Zhang’s presentation focused on data science for the next 50 years and how it evolved over the past years. She presented an interesting chart which listed highly sought skills like Python, R, Java, Scala, Hadoop, Spark and Hive among a number of other skills.
  • George Roumeliotis discussed design thinking for data scientists, he also touched upon integrating & coordinating IT with data science. He presented an interesting visual of subway map of data science skills.
  • Where to find Data Science Unicorns? Shlomo Engelson Argamon gave a lively talk about the theory, practice, management & ethics, and communication skills which make a good data scientist and shed light upon the different types of data science education offered today.
  • Jim Newswanger presented a glimpse of techniques & the value addition social media listening provides for a business e.g importance of identifying key tweets & followers on twitter.
  • Kirk Borne introduced the upcoming National Data Science Bowl competition and presented a picture of the last year’s competition, winners and encouraged audience participation.

Here's the full list of Invited Presentations:- View Speaker Bios & Agenda
  • "Education for data scientists - A survey of MOOCs" by Nitish R. Kanabar, Principal Data Scientist at Lend Street Financial, Inc.
  • "The other stuff: Turning machine learning into data science" by Brandon Rohrer Senior Data Scientist at Microsoft
  • "Off-boarding and Risk Management: protecting business value with a dynamic global workforce" by Nick Kadochnikov, Data Scientist at IBM
  • "What Unique Data Assets Reveal about Auto Insurance Shopping and Consumers" by John Ittner, Sr. Analytics Director and Qing Gu, Consultant at Transunion
  • "An Application of Data Science to Enhance the Efficiency of Employees of an Organization" by Dana Ferguson, Expert Data Scientist, Quantitative Research & Analytics at Allstate
  • "Data Science & Marketing: Turning Marketing into a Science" by Mike Jaron Data Scientist, Human/Social Dynamics at Google
  • "The Machine Learning Landscape" by Pratik Agrawal Senior Consultant (Data Science/Big Data), IRI
  • "Types of Data Science Education" by Shlomo Engelson Argamon, Master of Data Science Director, Illinois Institute of Technology
  • "Data Science Yesterday, Today and Tomorrow - exploring the driven forces behind Data Science" by Ling Zhang, Sr. Manager of Advanced Analytics - Data Scientist at Comcast
  • "User-Based Reviews and the PC Gaming Ecosystem: What It Takes to Survive in the Videogame World of 2015" by Scott Burger, Data Scientist at Microsoft
  • "Online Controlled Experiments: The Challenge of Trustworthy Results" by Randal Henne, Principal Data Scientist Manager, Analysis and Experimentation at Microsoft
  • "Data Gravity & Distributed Analytics" by John Thompson, General Manager - Advanced Analytics at Dell
  • " Agile Methodologies for Data Science Projects" by Shavar Beckford, Data Scientist at Allstate
  • "Machine learning and AI: The difference between knowledge and action" by Don Dini, Principal Data Scientist at AT&T
  • "Using Big Data Analytics to Create a More Intelligent and Personalized Web" by Bamshad Mobasher Prof. DePaul University (Computer Science), Director-Center for Web Intelligence
  • "How data science evolves and how it becomes 'secret' weapon in business" by Patrick Zhao, Business Intelligence Data Scientist at Amazon
  • "An approach to leading Data Science initiatives in corporate settings that maximizes the chance of success" by George Roumeliotis, Distinguished Data Scientist at Walmart
  • The Journey of Data: Understanding and preparing data for advanced analytics, Meltem Ballan, Sr. Data Scientist at Southwest Airlines
  • Direct Exposure at Default Models in Credit Risk by Edward Tong, Vice President, Model Risk Management at Bank of America
  • Science in Data Science-Text Mining, Tanay Chowdhury, Data Scientist at Zurich North America
  • Social Business Listening Techniques -- IBM Case Studies, Jim Newswanger, Sr Research Manager, Corporate Social Analytics, IBM
  • The National Data Science Bowl: Goals, Data, and How to Participate, Kirk Borne, Principal Data Scientist, Booz Allen Hamilton

Next Year Conference The Data Science Conference 2016 is on April 21-22, 2016 at the McCormick Place hosting upto 250 attendees. Stay Tuned!