Sat.Mar 17, 2018 - Fri.Mar 23, 2018

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Closing Data's Last-Mile Gap: Visualizing For Impact!

Occam's Razor

I worry about data’s last-mile gap a lot. As a lover of data-influenced decision making, perhaps you worry as well. A lot of hard work has gone into collecting the requirements and implementation. An additional massive investment was made in the effort to perform ninja like analysis. The end result was a collection trends and insights. The last-mile gap is the distance between your trends and getting an influential company leader to take action.

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Think 2018: Our favorite highlights from Wednesday

IBM Big Data Hub

Think 2018 is in full swing. We’re inspired hearing from leaders across industries using analytics to transform their business. And we’re thrilled to take part in conversations about data science, machine learning, AI and much more. Here are some highlights from Wednesday at Think.

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Strata Data San Jose March 2018 Musings

Sanjeev Mohan

Ladies and Gentlemen, we have a new buzz word for 2018: Operationalization ! I attended this year’s edition of Strata Data show last week in San Jose. Last year when I attended the show, it was still called Strata Hadoop. Interestingly, if last year many companies were trying to distance themselves from Hadoop, this year I witnessed outright hostility towards it.

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The ‘Scary’ Seven: big data challenges and ways to solve them

ScienceSoft

Big data can drive your company to success, but first you’ll need to deal with 7 major big data challenges. Find out what they are and how to solve them.

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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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4 Steps to getting started with data products

Juice Analytics

Over the years, we’ve had the pleasure to work with many great individuals and companies and through our work have gained the ability to sympathize with their experiences of what we like to call “going from 0 to 100." No, we’re not endorsing excessive speeding in your car. We’re talking about going from having nothing but hopes and dreams about delivering engaging analytics (0) to having an interactive data story that your users don’t want to put down (100).

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Think 2018: Our favorite highlights from Thursday

IBM Big Data Hub

Hard to believe we've arrived at the last day of Think 2018. From keynotes to panels, informal collaborations and learning sessions, we've witnessed first-hand the excitement that conversations about data and analytics bring to business.

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Engineering Data Science at Automattic

Data Science and Beyond

A post I’ve written on applying some software engineering best practices to data science projects. Data for Breakfast. Most data scientists have to write code to analyze data or build products. While coding, data scientists act as software engineers. Adopting best practices from software engineering is key to ensuring the correctness, reproducibility, and maintainability of data science projects.

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The Perils of 3 (or more)-Tier Infrastructure

Nutanix

Once upon a time, a long time ago (in 1999), 3-tier infrastructure was the go to for the needs of applications…from 1999.

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Today at Think 2018: What you can’t miss Thursday

IBM Big Data Hub

Hard to believe, but we’ve arrived at the final day of Think 2018. It’s been thrilling to be part of the energy flowing through the Cloud & Data Campus. We’ve seen an unprecedented level of engagement around analytics and the future of data-driven decision-making. But we’re not done yet. Here are our top picks for analytics pros today.

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Compliance bias in mobile experiments

The Unofficial Google Data Science Blog

by DANIEL PERCIVAL Randomized experiments are invaluable in making product decisions, including on mobile apps. But what if users don't immediately uptake the new experimental version? What if their uptake rate is not uniform? We'd like to be able to make decisions without having to wait for the long tail of users to experience the treatment to which they have been assigned.

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The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Communication

Speaker: David Bard, Principal at VP Product Coaching

In the fast-paced world of digital innovation, success is often accompanied by a multitude of challenges - like the pitfalls lurking at every turn, threatening to derail the most promising projects. But fret not, this webinar is your key to effective product development! Join us for an enlightening session to empower you to lead your team to greater heights.

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Logarithmic Confusion

Perceptual Edge

We typically think of quantitative scales as linear, with equal quantities from one labeled value to the next. For example, a quantitative scale ranging from 0 to 1000 might be subdivided into equal intervals of 100 each. Linear scales seem natural to us. If we took a car trip of 1000 miles, we might imagine that distance as subdivided into ten 100 mile segments.

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Augmented Data Discovery for Business Users

Smarten

Is Augmented Analytics Too Complex for Business Users? The concept of Augmented Analytics may sound complicated (and it may, in fact, be a challenge for a software vendor to accomplish), but the new augmented data discovery tools are quite easy to use. Unlike the data discovery tools of old, business users do not need to look to the IT staff or data scientists to perform advanced data discovery.

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Think 2018: Our favorite highlights from Tuesday

IBM Big Data Hub

The excitement, insights and innovation at Think 2018 is truly astounding. Today we heard from IBM Chairman, President and CEO, Ginni Rometty, plus industry leaders and clients who are transforming whole business sectors.

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Today at Think 2018: What you can’t miss Monday

IBM Big Data Hub

We’re live at Think 2018. If you’re joining us in Las Vegas, welcome to the biggest IBM event of the year. You’re about to experience a whirlwind of analytics keynotes, panels, demos, and more.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

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Think 2018: Our favorite highlights from Monday

IBM Big Data Hub

Think 2018 is the biggest IBM conference of the year covering all things tech. And, to be sure you don't miss a moment, here are highlights from Monday, March 19, the first day of the event.

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Today at Think 2018: What you can’t miss Wednesday

IBM Big Data Hub

Are you ready for another exciting day of Think 2018? There’s a big day planned for you at the Cloud & Data Campus and we’re here to help you navigate the many exciting sessions. Here are our top analytics picks of the day.

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Today at Think 2018: What you can’t miss Tuesday

IBM Big Data Hub

The second day of Think 2018 is here, and there’s an abundance of things to see and do at the Cloud & Data Campus. Here are the not-to-miss Think 2018 keynote speeches, demos, panels, training labs and other hands-on activities for Tuesday, March 20, both at the event and by tuning in online.

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