Some of the featured topics at Data Day.
Here is a rough guide to the topics covered at Data Day. We hope to have the time and room numbers up in the next day or so.
This is not all of the talks. We are still waiting on the flight itinerary and the final abstracts for a few of the speakers.
A/B Testing
Nelson Ray (Open Door) : When A/B Testing Fails: A Case Study in Real Estate
Apache Cassandra
Michelle Casbon (Qordoba) : Untangling the Ball of Strings: Machine Learning for Localization
Dor Laor (ScyllaDB) : ScyllaDB dance like butterfly sting like bee
Patrick McFadin (DataStax) : Choose your own Time Series adventure
2:00pm - Patrick McFadin (DataStax) : Time for a new relation: Going from RDBMS to Graph
Apache Spark
Michelle Casbon (Qordoba) : Untangling the Ball of Strings: Machine Learning for Localization
Gergely Svigruha : Demography Estimation on a Large Telco Graph – a Case Study
11:30am - Mo Patel (Think Big (Teradata)) : Traversing our way through Spark GraphFrames and GraphX
5:10pm - Borislav Iordanov (GRAKN.AI) : Large Scale Graph Analytics Through Graql
Artificial Intelligence
Jonathan Mugan (Deep Grammar) : How to Progress from NLP to Artificial Intelligence
Data and the Public Sphere
Jonathon Morgan (New Knowledge) : Truth is Dead
9:50am - William Lyon (Neo4j) : Make Graphs Great Again - Analyzing Election Data Using Neo4j
Data Science
Tim Gasper : Robot farmers and chefs: In the field and in your kitchen
Ryan Mitchell (LinkeDrive, Inc) : Web Scraping in a JavaScript World
Melissa Santos (Big Cartel) : Distances, Similarities, and Scores: Practical Model Examples
Stefan Krawczyk (Stitch Fix) : Scaling Data Science at Stitch Fix
Michelle Casbon (Qordoba) : Untangling the Ball of Strings: Machine Learning for Localization
Nelson Ray (Open Door) : When A/B Testing Fails: A Case Study in Real Estate
Brendan Herger (Capital One) : Machine Learning with Opponents
9:50am - Juliet Hougland (Cloudera) : How to Observe: Lessons from Epidemiologists, Actuaries and Charlatans
Data Visualization
9:50am - Corey Lanum (Cambridge Intelligence) : Graphs in time and space: A visual example
1:10pm - William Lyon (Neo4j) : Neo4j Graph Database Workshop For The Data Scientist Using Python
2:00pm - Chris LaCava (Expero) : Meaningful User Experience with Graph Data
Data Wrangling
Ryan Mitchell (LinkeDrive, Inc) : Web Scraping in a JavaScript World
Databases
Dor Laor (ScyllaDB) : ScyllaDB dance like butterfly sting like bee
Paul Dix (Influx) : Towards an open standard for metrics and time series
Paul Dix (Influx) : Structured Logs with InfluxDB - Log management at scale without full text search
Data Ops
Laine Campbell (N/A) : Database Reliability Engineering
Deep Learning
Brendan Herger (Capital One) : Machine Learning with Opponents
Pierre Gutierrez (Dataiku) : Pragmatic Deep Learning for image labelling. An application to a travel recommendation engine
DevOps
Laine Campbell (N/A) : Database Reliability Engineering
Docker
Michelle Casbon (Qordoba) : Untangling the Ball of Strings: Machine Learning for Localization
Stefan Krawczyk (Stitch Fix) : Scaling Data Science at Stitch Fix
Graph Databases - Cypher
1:10pm - William Lyon (Neo4j) : Neo4j Graph Database Workshop For The Data Scientist Using Python
2:50pm - Juan Sequeda (Capsenta) : Graph Query Languages
Graph Databases - General
Ted Wilmes (Expero) : Time Series and Audit Trails: Modeling Time in an Industrial Equipment Property Graph
9:50am - Corey Lanum (Cambridge Intelligence) : Graphs in time and space: A visual example
9:50am - Juan Sequeda (Capsenta) : Do I need a Graph Database
10:40am - Dave Bechberger (Expero) : Moving Your Data To Graph
11:30am - Luca Garulli (OrientDB) : Graph Databases: what's next?
11:30am - Mo Patel (Think Big (Teradata)) : Traversing our way through Spark GraphFrames and GraphX
1:10pm - Denise Gosnell (PokitDok) : Graphs vs Tables: Ready? Fight.
2:00pm - Patrick McFadin (DataStax) : Time for a new relation: Going from RDBMS to Graph
2:00pm - Chris LaCava (Expero) : Meaningful User Experience with Graph Data
2:50pm - Alaa Mahmoud (IBM) : Building a Graph Database in the Cloud: challenges and advantages
2:50pm - Juan Sequeda (Capsenta) : Graph Query Languages
2:50pm - David Mizell (Cray) : LEBM: Making a Thoroughly Nasty Graph Database Benchmark
4:20pm - Haikal Pribad (GRAKN.AI) : How to Manage and Harness Large-Scale Graph Data with Grakn
5:10pm - Borislav Iordanov (GRAKN.AI) : Large Scale Graph Analytics Through Graql
Graph Databases - Multimodal
9:50am - Juan Sequeda (Capsenta) : Do I need a Graph Database
10:40am - Jason Plurad (IBM) : Enabling a Multimodel Graph Platform with Apache TinkerPop
11:30am - Luca Garulli (OrientDB) : Graph Databases: what's next?
2:50pm - Juan Sequeda (Capsenta) : Graph Query Languages
Graph Databases - Neo4j
9:50am - William Lyon (Neo4j) : Make Graphs Great Again - Analyzing Election Data Using Neo4j
10:40am - Ryan Boyd (Neo4j) : Graphs + Sensors = The Internet of Connected Things
1:10pm - William Lyon (Neo4j) : Neo4j Graph Database Workshop For The Data Scientist Using Python
1:10pm - Denise Gosnell (PokitDok) : Graphs vs Tables: Ready? Fight.
Graph Databases - Tinkerpop
10:40am - Jason Plurad (IBM) : Enabling a Multimodel Graph Platform with Apache TinkerPop
11:30am - Mike Downie (Expero) : Implementing Network Algorithms in TinkerPop's GraphComputer
2:50pm - Alaa Mahmoud (IBM) : Building a Graph Database in the Cloud: challenges and advantages
Graph Databases - Use Cases
Gergely Svigruha : Demography Estimation on a Large Telco Graph – a Case Study
Humans in the Loop
Nicholas Gaylord (Crowdflower) : Machine learning with humans in the loop
Gabor Melli : Predictive Models for Inter-Linking Text-Rich Semantic Databases
Image Recognition
Pierre Gutierrez (Dataiku) : Pragmatic Deep Learning for image labelling. An application to a travel recommendation engine
IoT / Sensors
Tim Gasper : Robot farmers and chefs: In the field and in your kitchen
Ted Wilmes (Expero) : Time Series and Audit Trails: Modeling Time in an Industrial Equipment Property Graph
10:40am - Ryan Boyd (Neo4j) : Graphs + Sensors = The Internet of Connected Things
Machine Learning
Nicholas Gaylord (Crowdflower) : Machine learning with humans in the loop
Brendan Herger (Capital One) : Machine Learning with Opponents
Tim Gasper : Robot farmers and chefs: In the field and in your kitchen
Michelle Casbon (Qordoba) : Untangling the Ball of Strings: Machine Learning for Localization
Natural Language Processing
Tim Gasper : Robot farmers and chefs: In the field and in your kitchen
Michelle Casbon (Qordoba) : Untangling the Ball of Strings: Machine Learning for Localization
Jason Kessler (CDK) : Scattertext: A Tool for Visualizing Differences in Language
Jonathan Mugan (Deep Grammar) : How to Progress from NLP to Artificial Intelligence
Rob McDaniel (Live Stories) : Bootstrapping a corpus: how to build a rich topic library from a handful of words
Jacob Su Wang (OjoLabs) : Exploring Modeling Methods in Named Entity Recognition
Sanghamitra Deb (Accenture) : Creating Knowledgebases from text in absence of training data
Gabor Melli : Predictive Models for Inter-Linking Text-Rich Semantic Databases
Jonathon Morgan (New Knowledge) : Truth is Dead
Neural Networks
Alex Korbonits (Remitly) : Distilling dark knowledge from neural networks
Brendan Herger (Capital One) : Machine Learning with Opponents
Ontologies
Gabor Melli : Predictive Models for Inter-Linking Text-Rich Semantic Databases
4:20pm - Haikal Pribad (GRAKN.AI) : How to Manage and Harness Large-Scale Graph Data with Grakn
Python
Ryan Mitchell (LinkeDrive, Inc) : Web Scraping in a JavaScript World
Jason Kessler (CDK) : Scattertext: A Tool for Visualizing Differences in Language
1:10pm - William Lyon (Neo4j) : Neo4j Graph Database Workshop For The Data Scientist Using Python
Recommender Systems
Pierre Gutierrez (Dataiku) : Pragmatic Deep Learning for image labelling. An application to a travel recommendation engine
Time Series Data
Patrick McFadin (DataStax) : Choose your own Time Series adventure
Paul Dix (Influx) : Towards an open standard for metrics and time series
Paul Dix (Influx) : Structured Logs with InfluxDB - Log management at scale without full text search
Ted Wilmes (Expero) : Time Series and Audit Trails: Modeling Time in an Industrial Equipment Property Graph
Training Data
Melissa Santos (Big Cartel) : Distances, Similarities, and Scores: Practical Model Examples
Alex Korbonits (Remitly) : Distilling dark knowledge from neural networks
Jacob Su Wang (OjoLabs) : Exploring Modeling Methods in Named Entity Recognition
Sanghamitra Deb (Accenture) : Creating Knowledgebases from text in absence of training data
Nicholas Gaylord (Crowdflower) : Machine learning with humans in the loop
Pierre Gutierrez (Dataiku) : Pragmatic Deep Learning for image labelling. An application to a travel recommendation engine
