Python's popularity for data science use cases has skyrocketed in recent years due to its ease of use, great developer and user community, and solid core of scientific libraries. Christine Doig explores data science and the state of the Python ecosystem and helps navigate the large amount of open source libraries available for data science in Python, providing a map to guide you on the journey.

While many companies struggle to adopt big data, a number of industry leaders are leapfrogging big data adoption by going straight to automating core business processes. Andreas Schmidt presents examples from leading European companies that have overcome cultural, technical, and scientific challenges and unlocked the potential of big data in an entirely different way.

Hadoop is famously scalable, as is cloud computing. R, the thriving and extensible open source data science software. . .not so much. Mario Inchiosa and Roni Burd outline how to seamlessly combine Hadoop, cloud computing, and R to create a scalable data science platform that lets you explore, transform, model, and score data at any scale from the comfort of your favorite R environment.

So many of the data projects making headlinesfrom a new app for finding public services to a new probabilistic model for predicting weather patterns for subsistence farmersare great accomplishments but dont seem to have end users in mind. Discover how organizations are designing with, not for, people, accounting for what drives them in order to make long-lasting impact.

So many of the data projects making headlinesfrom a new app for finding public services to a new probabilistic model for predicting weather patterns for subsistence farmersare great accomplishments but dont seem to have end users in mind. Discover how organizations are designing with, not for, people, accounting for what drives them in order to make long-lasting impact.

Data scientists inhabit such an ever-changing landscape of languages, packages, and frameworks that it can be easy to succumb to tool fatigue. If this sounds familiar, you may have missed the increasing popularity of Linux containers in the DevOps world, in particular Docker. Michelangelo D'Agostino demonstrates why Docker deserves a place in every data scientists toolkit.

It is fashionable today to declare doom and gloom for the data lake. Alex Gorelik discusses best practices for Hadoop data lake success and provides real-world examples of successful data lake implementations in a non-vendor-specific talk.

Youve decided you need data scientists. You know who to hire. Now, what do you do with them? Yael Garten offers examples of how companies like LinkedIn use data to make business and product decisions. Yael reviews the spectrum of data science, and discusses the culture, process and tools needed to transform companies into data-driven organizations.

Despite Python's popularity throughout the data-engineering and data science workflow, the principles behind its performance and scaling behavior are less understood. Travis Oliphant explains best practices and modern tools to scale Python to larger-than-memory and distributed workloads without sacrificing its ease of use or being forced to adopt heavyweight frameworks.

Increasing competition and technological change is impelling the telco industry toward a new model of analytics. Telefnica has been at the front of this change, driving business transformation to a digital telco. John Belchamber and Arturo Canales tell the story of that transformation and detail the pitfalls and challenges faced by teams looking to follow a similar journey.

Even though each of us is only 1 of 7 billion, we all want to feel special. Product personalization is transforming the Internet experience to be all about me (and you too!). Yao Morin explores how to create customer-centric experiences through data science and software engineering, using Intuit TurboTax as a case study.