"data scientist" entries

Strata Newsletter: January 19, 2012

Strata Newsletter: January 19, 2012

Data scientists need agility and an entrepreneurial outlook. Plus: data news of note.

Highlights from the 1/19/12 edition of the Strata newsletter include: Strata chair Edd Dumbill explains what big data is, what it does and why it matters.

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The number one trait you want in a data scientist

The number one trait you want in a data scientist

DJ Patil on the traits of data scientists and how data science will evolve within companies.

DJ Patil, data scientist in residence at Greylock Partners, discusses the key trait data scientists need and the obstacles data scientists face within organizations.

Comment: 1
Building data science teams

Building data science teams

Data science teams need people with the skills and curiosity to ask the big questions.

A data science team needs people with the right skills and perspectives, and it also requires strong tools, processes, and interaction between the team and the rest of the company.

Comments: 12
Citizen science, civic media and radiation data hint at what’s to come

Citizen science, civic media and radiation data hint at what’s to come

The evolution of Safecast is a glimpse into networked accountability.

After a tsunami caused a nuclear disaster in Japan, a radiation detection network starting aggregating and publishing data. The result, Safecast, shows how citizen science and open data are changing our understanding of the world.

Comment: 1
Citizen science, civic media and radiation data hint at what's to come

Citizen science, civic media and radiation data hint at what's to come

The evolution of Safecast is a glimpse into networked accountability.

After a tsunami caused a nuclear disaster in Japan, a radiation detection network starting aggregating and publishing data. The result, Safecast, shows how citizen science and open data are changing our understanding of the world.

Comment: 1

Why the term “data science” is flawed but useful

Counterpoints to four common data science criticisms.

While formal boundaries and professional criteria for "data science" remain undefined, here's why we should keep using the term.

Comments: 9

Why the term "data science" is flawed but useful

Counterpoints to four common data science criticisms.

While formal boundaries and professional criteria for "data science" remain undefined, here's why we should keep using the term.

Comments: 9
3 skills a data scientist needs

3 skills a data scientist needs

LinkedIn's Pete Skomoroch on the key capabilities of data scientists.

In this brief video interview, LinkedIn senior research scientist Pete Skomoroch reveals the three core skills of data scientists.

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The data analysis path is built on curiosity, followed by action

Why simplicity, empiricism, and DIY are keys to data analysis.

Precision and preparation define traditional data analysis, but author Philipp K. Janert believes there's more to it than just that. In this interview, he explains how simplicity, experimentation and action can shape data work.

Comment: 1
What is data science?

What is data science?

The future belongs to the companies and people that turn data into products.

This report takes a detailed look at the promise of data science and the technologies and unique skill sets at the heart of this growing discipline.

Comments: 54