Sat.Aug 25, 2018 - Fri.Aug 31, 2018

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Sea change: What happens when Jupyter becomes pervasive at a university?

O'Reilly on Data

Fernando Perez talks about UC Berkeley's transition into an environment where many undergraduates use Jupyter and the open data ecosystem as naturally as they use email. Continue reading Sea change: What happens when Jupyter becomes pervasive at a university?

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GDPR and a history of regulation-driven innovation

IBM Big Data Hub

Changes in rules and regulations create a fertile environment for innovation, in sports and in business. You just have to know where to look and approach things with an open mind.

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How Fannie Mae is Creating a Modern Data Environment

Bruno Aziza

Fannie Mae has established a modern data environment which results in a richer and more granular customer experience.

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Driving Digital Strategy (Review)

DSI Analytics

Many companies fail, despite their best efforts, at reinventing themselves in the digital age. Harvard’s Sunil Gupta, who has worked with top organizations around the globe, presents valuable lessons-learned in his recent book, Driving Digital Strategy: A Guide to Reimagining Your Business. This post is a summary and review of that book. Introduction: I really liked the […].

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How To Get Promoted In Product Management

Speaker: John Mansour

If you're looking to advance your career in product management, there are more options than just climbing the management ladder. Join our upcoming webinar to learn about highly rewarding career paths that don't involve management responsibilities. We'll cover both career tracks and provide tips on how to position yourself for success in the one that's right for you.

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Disease prediction using the world's largest clinical lab data set

O'Reilly on Data

Cristian Capdevila explains how Prognos is predicting disease. Continue reading Disease prediction using the world's largest clinical lab data set.

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Winning with AI: Industry POV on how to change the game, part 1

IBM Big Data Hub

Will artificial intelligence remake business as we know it? Will AI change the way we do our jobs – or reshape entire categories of careers? How can businesses harness the transformative potential of AI and outplay the competition? Leaders from a broad range of industries and expertise will be discussing these questions at “Changing the Game: Winning with AI,” a 13 September, 2018 event taking place live in New York City.

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In-depth with CDO Christopher Bannocks

Peter James Thomas

Part of the In-depth series of interviews. Today I am talking to Christopher Bannocks , who is Group Chief Data Officer at ING. ING is a leading global financial institution, headquartered in the Netherlands. As stressed in other recent In-depth interviews [1] , data is a critical asset in banking and related activities, so Christopher’s role is a pivotal one.

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Building accessible tools for large-scale computation and machine learning

O'Reilly on Data

The O’Reilly Data Show Podcast: Eric Jonas on Pywren, scientific computation, and machine learning. In this episode of the Data Show , I spoke with Eric Jonas , a postdoc in the new Berkeley Center for Computational Imaging. Jonas is also affiliated with UC Berkeley’s RISE Lab. It was at a RISE Lab event that he first announced Pywren , a framework that lets data enthusiasts proficient with Python run existing code at massive scale on Amazon Web Services.

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Specificity is the Soul of Data Narrative

Juice Analytics

The folks in the front of the room stared with a forced intensity at (what must have been) the 23rd straight slide showing data about website performance. Their glazed eyes would have been entirely evident if the speaker wasn’t so intently focused on pointing out the change in bounce rate between August and July. In the back of the room, Brian wasn’t able to summon the energy to care.

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Meet the newest Data Superheros: The Sixth Annual Data Impact Awards Finalists Are…

Cloudera

Drum roll… Starting from well over 100 nominations, we are excited to announce the finalists for this year’s Data Impact Awards ! Each year, nominees have raised the bar, and this year is no exception. The level of impact that organizations have shown and the variety of use cases are inspiring. From AI models that power retail customer decision engines to utility meter analysis that disables underperforming gas turbines, these finalists demonstrate how machine learning and analytics have become

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Navigating the Future: Generative AI, Application Analytics, and Data

Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

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Winning with AI: Industry POV on how to change the game, part 2

IBM Big Data Hub

Ahead of the “Change the Game: Winning with AI,” a September, 13, 2018 event, we asked a virtual roundtable of analytics experts and analysts to wrestle with some of the core issues surrounding AI.

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Jupyter notebooks and the intersection of data science and data engineering

O'Reilly on Data

David Schaaf explains how data science and data engineering can work together to deliver results to decision makers. Continue reading Jupyter notebooks and the intersection of data science and data engineering.

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7 keys to self-service BI success

CIO Business Intelligence

Self-service business intelligence (BI) is becoming more popular, as organizations look for ways to make it easier for business users at all levels to glean insights from growing volumes of data.

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Introducing Cloudera Enterprise 6.0

Cloudera

Digital technologies are changing business models, reshaping how companies go-to-market, win new customers and drive new revenue-producing opportunities. Consider the following practices that, until recently, were relegated to the R&D department: Data-driven decision making – the collection and analysis of data to guide decisions that improve success.

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Get Better Network Graphs & Save Analysts Time

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

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How consistent data governance helps eliminate "Garbage In, Garbage Out"

IBM Big Data Hub

Are your efforts to leverage big data hampered by poor or outdated data management? .

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Data science as a catalyst for scientific discovery

O'Reilly on Data

Michelle Gill discusses how data science methods and tools can link information from different scientific fields and accelerate discovery. Continue reading Data science as a catalyst for scientific discovery.

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In the Multi-Cloud Era, Cloud Cost Optimization is an Engineering Problem

Nutanix

A key technology trend we’ve seen recently in enterprise organizations is the increasing adoption of multiple cloud environments — combining on-premises private clouds and public cloud services.

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Data Science Challenges – It’s Deja Vu all over again!

Peter James Thomas

The rather famous tautology, “It’s déjà vu all over again”, has of course been ascribed to that darling of malapropisms, baseball catcher Yogi Berra [1]. The phrase came to mind for me today when coming across the following exhibit: © Business Over Broadway (2018). Based on Kaggle’s State of Data Science Survey 2017 (Sample size: 10,153).

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Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

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Natural Language Processing for Advanced Analytics!

Smarten

Does Natural Language Processing Work with Advanced Analytics? Natural Language Processing (NLP) is a trend that has taken over technology. Vendors are applying this technique to all types of searching and Advanced Analytics is no exception. Nearly every enterprise is working on data democratization and wants to transform its business users into Citizen Data Scientists with easy-to-use tools that offer sophisticated analytical techniques and simple access and analysis – no matter the skill

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Beyond interactive: Scaling impact with notebooks at Netflix

O'Reilly on Data

Michelle Ufford shares how Netflix leverages notebooks today and describes a brief vision for the future. Continue reading Beyond interactive: Scaling impact with notebooks at Netflix.

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The Only Hypervisor Certified for Production SAP HANA on Hyperconverged Infrastructure (HCI): Another First for Nutanix AHV

Nutanix

At Nutanix we strive every day to make infrastructure just a little bit better than it was yesterday. Over the last eight years, Nutanix has reinvented storage, reinvented the hypervisor, and created a delightful private cloud for virtualized workloads that makes the lives of our customers better.

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The future of data-driven discovery in the cloud

O'Reilly on Data

Ryan Abernathey makes the case for the large-scale migration of scientific data and research to the cloud. Continue reading The future of data-driven discovery in the cloud.

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Understanding User Needs and Satisfying Them

Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

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Democratizing data

O'Reilly on Data

Tracy Teal explains how to bring people to data and empower them to address their questions. Continue reading Democratizing data.

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