March, 2017

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Artificial Intelligence: Implications On Marketing, Analytics, And You

Occam's Razor

A rare post today. It looks a little further out into the future than I normally tend to. It attempts to simplify a topic that has more than it’s share of coolness, confusion and complexity. While the phrase Artificial Intelligence has been around since the first human wondered if she could go further if she had access to entities with inorganic intelligence, it truly jumped the shark in 2016.

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Product And Platform

Bruno Aziza

Products do not often exhibit long-term disruptive value. Platforms do. From Snap to Amazon, companies that invested in underlying platform technology were able to exert lasting influence over the way in which people consume content and information.

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Beware the Data Governance Ides of March

Jim Harris

Morte de CĂ©sare (Death of Caesar) by Vincenzo Camuccini , 1798 Today is the Ides of March (March 15), which back in 44 BC was definitely not a good day to be Julius Caesar, who was literally stabbed in the back by the Roman Senate during his assassination in the Theatre of Pompey (as depicted above), which was spearheaded by Brutus and Cassius in a failed attempt to restore the Roman Republic, but instead resulted in a series of civil wars that ultimately led to the establishment of the permanen

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Office Politics: A survivor’s guide for programmers and data scientists

DSI Analytics

Office Politics: A survivor’s guide for data scientists Everyone gets sunk by office politics at some point in their career, but data scientists are in some ways especially ill-prepared to navigate the unspoken rules and hidden agendas that together form a critical part of the corporate world. There are those who leverage office politics as […].

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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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Top Data Science Resources on the Internet right now

MLWhiz

I have been looking to create this list for a while now. There are many people on quora who ask me how I started in the data science field. And so I wanted to create this reference. To be frank, when I first started learning it all looked very utopian and out of the world. The Andrew Ng course felt like black magic. And it still doesn’t cease to amaze me.

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Artificial Intelligence Just Got Even Smarter

TIBCO

Little can rival the ascendancy of Artificial Intelligence’s (AI) profile from a niche concern to technology’s hottest theme, transcending the pages of the technical press to sit at the heart of mainstream culture. A certain breed of robotics has been a dominant force in this traction bringing machine learning to the masses in the form of chatbots and avatars that feature in our homes and customer service experience, as well as in banking and call centers.

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The Art Of Building Platforms

Bruno Aziza

You do not build a product in a vacuum. We build platforms with highly cross-functional teams that include people from partnerships, policy, support, engineering, product marketing, analytics, design, research, and many other groups.

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Nutanix Xpress: The Little HCI Engine That Could

Nutanix

This morning on May 24th, 2016, we announced Nutanix Xpress – our first product line dedicated just to small and medium-sized businesses.

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Attributing a deep network’s prediction to its input features

The Unofficial Google Data Science Blog

By MUKUND SUNDARARAJAN, ANKUR TALY, QIQI YAN Editor's note: Causal inference is central to answering questions in science, engineering and business and hence the topic has received particular attention on this blog. Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs.

IT 68
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Basics Of Linear Regression

MLWhiz

Today we will look into the basics of linear regression. Here we go : Contents Simple Linear Regression (SLR) Multiple Linear Regression (MLR) Assumptions 1. Simple Linear Regression Regression is the process of building a relationship between a dependent variable and set of independent variables. Linear Regression restricts this relationship to be linear in terms of coefficients.

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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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The FCC Just Made Your Online World Less Private

Bruno Aziza

A new White House administration and Congress are moving quickly on high-profile policy like immigration and health care – issues that are dominating the headlines. Another potential policy shift that is getting much less mainstream media attention could threaten the privacy of nearly every American.

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7 Key Facts You Need to Know Before Investing In Drone Technology

Bruno Aziza

With a broad range of practical applications and rapidly evolving technology, drones offer huge untapped potential, but not every market offers equal opportunities for growth. Here are seven facts and forecasts to know before investing.

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Avoiding Costly Recruiting Mistakes: The Importance Of High Standards

Bruno Aziza

MongoDB CEO Dev Ittycheria explains how expensive poor hiring decisions are and why the devil you know may be better than the devil you don’t.

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Top advice for a Data Scientist

MLWhiz

A data scientist needs to be Critical and always on a lookout of something that misses others. So here are some advices that one can include in day to day data science work to be better at their work: 1. Beware of the Clean Data Syndrome You need to ask yourself questions even before you start working on the data. Does this data make sense? Falsely assuming that the data is clean could lead you towards wrong Hypotheses.

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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 Digital Disruption Transcends Industry Borders

Bruno Aziza

7 questions all business and IT leaders should be asking.

IT 59
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Act II: Thinking Global, Acting Local

Nutanix

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