Darkhorse

SEPTEMBER 26, 2014

The poor, maligned 3D pie chart. He is so popular among the common folk, but put him next to his peers and his vacant stare betrays (not entirely unfounded) feelings of insecurity and inadequacy. Sometimes the only way to address such feelings is to let go of your inhibitions and do something unexpected. He has value hidden away, we're sure of it. And so, for the third installment in our Data Looks Better Naked series, we are recommending that the 3D pie do what the bar chart and table have done

IT 112

IT Visualization 112

Data Science and Beyond

SEPTEMBER 19, 2014

This is the third part of a series of posts on my Bandcamp recommendations (BCRecommender) project. Check out the first part for the general motivation behind this project and the second part for the system architecture. The main goal of the BCRecommender project is to help me find music I like. This post discusses the algorithmic approaches I took towards that goal.

Modeling 52

Modeling 52

Wise Analytics

SEPTEMBER 30, 2014

The role of governed data discovery is becoming increasingly important as organizations manage more complex and diverse data that they want to gain insights from. Self-service BI access and broader data discovery capabilities means that BI is deployed to more users who leverage data in the way that best suits them and not according to pre-defined analytics.

Management 40

Management Data Governance Data Warehouse Data Quality 40

MLWhiz

SEPTEMBER 29, 2014

THE PROBLEM: Recently I was working on the Criteo Advertising Competition on Kaggle. The competition was a classification problem which basically involved predicting the click through rates based on several features provided in the train data. Seeing the size of the data (11 GB Train), I felt that going with Vowpal Wabbit might be a better option. But after getting to an CV error of.47 on the Kaggle LB and being stuck there , I felt the need to go back to Scikit learn.

Advertising 40

Advertising 40

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.

Experimentation

Howard Dresner

SEPTEMBER 20, 2014

Perhaps a tag with “some assembly required” should be attached to business intelligence analytics tools. We just released in July our Advanced and Predictive Analytics Market Study report in our Wisdom of Crowds series, and I wanted to explore the topic in more depth in one of my recent Friday #BIWisdom tweetchats. Our market survey found that awareness of the importance of BI analytics is high (90 percent), but adoption of analytics tools is in the early stages of deployment even though many of

Business Intelligence 40

Business Intelligence Predictive Analytics Data-driven Data Processing 40

Nutanix

SEPTEMBER 29, 2014

There’s no question that enterprise application usability has for the most part been left behind.

Management 20

Management Enterprise 20

Data Science and Beyond

SEPTEMBER 7, 2014

This is the second part of a series of posts on my BCRecommender – personalised Bandcamp recommendations project. Check out the first part for the general motivation behind this project. BCRecommender is a hobby project whose main goal is to help me find music I like on Bandcamp. Its secondary goal is to serve as a testing ground for ideas I have and things I’d like to explore.

Testing 52

Testing IT 52

Wise Analytics

SEPTEMBER 23, 2014

BI implementations are becoming more commonplace as organizations realize they cannot overlook the management of and access to their data in order to facilitate better decision-making. What this means for many is the re-evaluation of resources, skill sets, and project planning to ensure that the proper resources exist to support BI and analytics development.

Consulting 40

Consulting Risk Data Analytics Analytics 40

MLWhiz

SEPTEMBER 27, 2014

This is part one of a learning series of pyspark, which is a python binding to the spark program written in Scala. The installation is pretty simple. These steps were done on Mac OS Mavericks but should work for Linux too. Here are the steps for the installation: 1. Download the Binaries: Spark : [link] Scala : [link] Dont use Latest Version of Scala, Use Scala 2.10.x 2.

40

40

Howard Dresner

SEPTEMBER 4, 2014

“If I’m at Starbucks doing business intelligence via WiFi with my laptop, is that Mobile BI? If so, if I do the same thing at work, what is that?” That question started the discussion at one of my recent Friday #BIWisdom tweetchats. When the tweetchat tribe tried to level set what this booming area of business intelligence really is, we found some differing opinions.

Business Intelligence 40

Business Intelligence Interactive Sales Cost-Benefit 40

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.

Management

Nutanix

SEPTEMBER 22, 2014

There’s no question that enterprise application usability has for the most part been left behind.

Enterprise 20

Enterprise 20

MLWhiz

SEPTEMBER 26, 2014

It has been some time since I was stalling learning Hadoop. Finally got some free time and realized that Hadoop may not be so difficult after all. What I understood finally is that Hadoop is basically comprised of 3 elements: A File System Map – Reduce Its many individual Components. Let’s go through each of them one by one. 1. Hadoop as a File System: One of the main things that Hadoop provides is cheap data storage.

IT 40

IT 40

Data Science and Beyond

SEPTEMBER 23, 2014

This is the fourth part of a series of posts on my Bandcamp recommendations (BCRecommender) project. Check out previous posts on the general motivation behind this project, the system's architecture, and the recommendation algorithms. Having used BCRecommender to find music I like, I’m certain that other Bandcamp fans would like it too. It could probably be extended to attract a wider audience of music lovers, but for now, just getting feedback from Bandcamp fans would be enough.

IT 40

IT 40

Wise Analytics

SEPTEMBER 18, 2014

This is a question I’ve been asking myself for a while. The data infrastructure exists to support Big Data, operational data streams, data quality practices, and the list goes on. Best practices exist for organizations to follow to achieve a strong information management framework and tie data to business processes enabling decision makers the ability to take actions on the insights they’ve gleaned.

Marketing 40

Marketing Data Quality Big Data Management 40

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.

Testing

Nutanix

SEPTEMBER 17, 2014

There’s no question that enterprise application usability has for the most part been left behind.

Enterprise 20

Enterprise 20

Nutanix

SEPTEMBER 13, 2014

There’s no question that enterprise application usability has for the most part been left behind.

Enterprise 20

Enterprise 20

Nutanix

SEPTEMBER 12, 2014

There’s no question that enterprise application usability has for the most part been left behind.

IT 20

IT Enterprise 20

Nutanix

SEPTEMBER 11, 2014

There’s no question that enterprise application usability has for the most part been left behind.

Enterprise 20

Enterprise 20

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Nutanix

SEPTEMBER 11, 2014

There’s no question that enterprise application usability has for the most part been left behind.

Enterprise 20

Enterprise 20

Nutanix

SEPTEMBER 8, 2014

There’s no question that enterprise application usability has for the most part been left behind.

Enterprise 20

Enterprise 20

Nutanix

SEPTEMBER 3, 2014

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20

Nutanix

SEPTEMBER 3, 2014

20

20

Speaker: Nicholas Zeisler, CX Strategist & Fractional CXO

The first step in a successful Customer Experience endeavor (or for that matter, any business proposition) is to find out what’s wrong. If you can’t identify it, you can’t fix it! 💡 That’s where the Voice of the Customer (VoC) comes in. Today, far too many brands do VoC simply because that’s what they think they’re supposed to do; that’s what all their competitors do.

Customer Analytics

Nutanix

SEPTEMBER 3, 2014

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