October, 2011

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Data Analysis 101: Seven Simple Mistakes That Limit Your Salary

Occam's Razor

Data analysis is not easy. It takes years to get good at it, and once you get good at it you realize how much more there is to learn. That is part of the joy. You are always learning. You are always growing. This blogpost is a collection of tips I share with my friends who are just starting out. Each tip is a "simple" mistake that is easily avoided.

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A New Building Block

Nutanix

I’m privileged to be involved with disruptive technologies very early on, sometimes at a stage when it may be hard to fully see the impact.

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Winning the Netflix Prize: A Summary

Edwin Chen

How was the Netflix Prize won? I went through a lot of the Netflix Prize papers a couple years ago, so I’ll try to give an overview of the techniques that went into the winning solution here. Normalization of Global Effects. Suppose Alice rates Inception 4 stars. We can think of this rating as composed of several parts: A baseline rating (e.g., maybe the mean over all user-movie ratings is 3.1 stars).

Metrics 101
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A New Building Block

Nutanix

I’m privileged to be involved with disruptive technologies very early on, sometimes at a stage when it may be hard to fully see the impact.

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From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

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VDI Series: Part 2 - Addressing Performance

Nutanix

At VMworld back in August, Nutanix was honored with TechTarget's Best of VMworld - Desktop Virtualization award.

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Best Social Media Metrics: Conversation, Amplification, Applause, Economic Value

Occam's Razor

I am going to break one of my unspoken cardinal rules: Only write about real problems and measurement that is actually possible in the real world. I am going to break the second part of the rule. I am going to define a way for you to think about measuring social media, and you can't actually easily measure what I am going to recommend. [Update: Please see update #2 below, you can now easily measure what's recommended in this post.].

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