Many large organizations want to develop data science capabilities, but the traditional complexity and legacy of such companies dont allow a fast and agile evolution toward data-driven decision making. EasyJet is working toward becoming completely data driven. Alberto Rey shares real-world examples on how easyJet is tackling the challenges of scaling up its analytics capabilities.

Eddie Copeland shares lessons learned from piloting the London Office of Data Analytics, a collaboration between the Greater London Authority, Nesta, and ASI Data Science that is exploring the potential of applying data analytics to reform public services.

What if you had to build more models than there are data scientists in the worlda feat enterprise companies serving hundreds of thousands of businesses often have to do? Leah McGuire offers an overview of Salesforce's general-purpose machine-learning platform that automatically builds per-company optimized models for any given predictive problem at scale, beating out most hand-tuned models.

M. C. Srivas covers the technologies underpinning the big data architecture at Uber and explores some of the real-time problems Uber needs to solve to make ride sharing as smooth and ubiquitous as running water, explaining how they are related to real-time big data analytics.

R is a top contender for statistics and machine learning, but Spark has emerged as the leader for in-memory distributed data analysis. Douglas Ashton, Aimee Gott, and Mark Sellors introduce Spark, cover data manipulation with Spark as a backend to dplyr and machine learning via MLlib, and explore RStudio's sparklyr package, giving you the power of Spark without having to leave your R session.

William Lyon explains how to use a graph database to generate real-time recommendations using real-world data. William introduces graph data modeling and querying concepts using Neo4j and Cypher, the query language for graphs to import and query data, before demonstrating how to apply graph algorithms and NLP using Python data science tools to enhance your recommendations.

Ready to dip your toe into data science? Va Barbosa explains why you should start with notebooks and PixieDust, a new open source library that helps data scientists and developers working in the Jupyter Notebook and Apache Spark be more efficient.

Jonathon Morgan explores computer vision, deep learning, and natural language processing techniques for uncovering communities of white nationalists and neo-Nazis on social media and identifying which ones are on the path to radicalization.

Big data and data science have great potential for accelerating business, but how do you reconcile the business opportunity with the sea of possible technologies? Data should serve the strategic imperatives of a businessthose aspirations that will define an organizations future vision. Scott Kurth and Edd Wilder-James explain how to create a modern data strategy that powers data-driven business.

The aviation industry is facing a huge pressure in costs as well as a profound disruption in marketing and service. With ticket revenues dropping, increasing customer loyalty is key. Andreas Ribbrock explains how Lufthansa German Airlines uses data science and data-driven decision making to create the next level of digital customer experience along the full customer journey.

Not all data science problems are big data problems. Lots of small and medium product companies want to start their journey to become data driven. Nischal HP and Raghotham Sripadraj share their experience building data science platforms for various enterprises, with an emphasis on making the right architecture choices and using distributed and fault-tolerant tools.

Food production and preparation have always been labor and capital intensive, but with the internet of things, low-cost sensors, cloud-computing ubiquity, and big data analysis, farmers and chefs are being replaced with connected, big data robotsnot just in the field but also in your kitchen. Tim Gasper explores the tech stack, data science techniques, and use cases driving this revolution.

David Talby and Claudiu Branzan offer a live demo of an end-to-end system that makes nontrivial clinical inferences from free-text patient records. Infrastructure components include Kafka, Spark Streaming, Spark, and Elasticsearch; data science components include spaCy, custom annotators, curated taxonomies, machine-learned dynamic ontologies, and real-time inferencing.

How do we know that an advertisement or promotion truly drives incremental revenue? Michelangelo D'Agostino and Bill Lattner share their experience developing machine-learning techniques for predicting treatment responsiveness from randomized controlled experiments and explore the use of these persuasion models at scale in politics, social good, and marketing.

Peng Du and Randy Wei offer an overview of Ubers data science workbench, which provides a central platform for data scientists to perform interactive data analysis through notebooks, share and collaborate on scripts, and publish results to dashboards and is seamlessly integrated with other Uber services, providing convenient features such as task scheduling, model publishing, and job monitoring.

Join in to learn how to do scalable, end-to-end data science in R on single machines as well as on Spark clusters. You'll be assigned an individual Spark cluster with all contents preloaded and software installed and use it to gain experience building, operationalizing, and consuming machine-learning models using distributed functions in R.

Zillow pioneered providing access to unprecedented information about the housing market. Long gone are the days when you needed an agent to get comparables and prior sale and listing data. And with more data, data science has enabled more use cases. Jasjeet Thind explains how Zillow uses Spark and machine learning to transform real estate.