Data Science and Beyond

OCTOBER 1, 2015

I recently gave a talk about recommender systems at the Data Science Sydney meetup (the slides are available here). This post roughly follows the outline of the talk, expanding on some of the key points in non-slide form (i.e., complete sentences and paragraphs!). The first few sections give a broad overview of the field and the common recommendation paradigms, while the final part is dedicated to debunking five common myths about recommender systems.

Data Science 69

Data Science 69

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. We describe experiment designs which have proven effective for us and discuss the subtleties of trying to generalize the results via modeling. A/B testing is used widely in information technology companies to guide product development and improvements.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

Jim Harris

OCTOBER 29, 2015

According to my high school English teacher, specificity is the key to effective communication and understanding. Unfortunately for managed service providers ( MSPs ) the term “managed services” does not communicate the specific services they offer, which makes it difficult for potential customers to understand the potential benefits of doing business with MSPs in general, let alone provide the information needed to differentiate one MSP from another in an increasingly crowded marketplace.

Cost-Benefit 40

Cost-Benefit Marketing Management Software 40

MLWhiz

OCTOBER 10, 2015

Yesterday I got introduced to awk programming on the shell and is it cool. It lets you do stuff on the command line which you never imagined. As a matter of fact, it’s a whole data analytics software in itself when you think about it. You can do selections, groupby, mean, median, sum, duplication, append. You just ask. There is no limit actually.

Software 40

Software Data Analytics Analytics IT 40

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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.

Visualization

Nutanix

OCTOBER 15, 2015

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

Enterprise 20

Enterprise 20

Data Science and Beyond

OCTOBER 18, 2015

I recently finished reading the book In Defense of Food: An Eater’s Manifesto by Michael Pollan. The book criticises nutritionism – the idea that one should eat according to the sum of measured nutrients while ignoring the food that contains these nutrients. The key argument of the book is that since the knowledge derived using food science is still very limited, completely relying on the partial findings and tools provided by this science is likely to lead to health issues.

Modeling 52

Modeling Measurement 52

MLWhiz

OCTOBER 8, 2015

Shell Commands are powerful. And life would be like hell without shell is how I like to say it(And that is probably the reason that I dislike windows). Consider a case when you have a 6 GB pipe-delimited file sitting on your laptop and you want to find out the count of distinct values in one particular column. You can probably do this in more than one way.

IT 40

IT 40

Nutanix

OCTOBER 15, 2015

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

Enterprise 20

Enterprise 20

Nutanix

OCTOBER 2, 2015

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

Technology 20

Technology Enterprise 20

Nutanix

OCTOBER 2, 2015

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

Technology 20

Technology Enterprise 20

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.

Management

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

by NIALL CARDIN, OMKAR MURALIDHARAN, and AMIR NAJMI When working with complex systems or phenomena, the data scientist must often operate with incomplete and provisional understanding, even as she works to advance the state of knowledge. This is very much what scientists do. Our post describes how we arrived at recent changes to design principles for the Google search page, and thus highlights aspects of a data scientist’s role which involve practicing the scientific method.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45