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Interview: Shalini Agarwal, Director, Engineering and Product at LinkedIn

I recently caught up with Shalini Agarwal, Director, Engineering and Product at LinkedIn, to discuss how we need more data scientists to make our applications smarter; however we can make them more efficient and accomplish more with data scientists by having automated workflows and tools. These tools can be used by non-data scientists to leverage the established workflows and remove the repetitive tasks from the mountain of tasks expected from a data-scientist.

The Myth of Entry-level Data Science

In this special guest feature, Kevin Safford, Sr. Director of Engineering for Umbel offers a no-nonsense look at how to answer the proverbial question “How can I become a data scientist.” To understand how to become a data scientist, it’s best to get on the same page on what data science is. And if this is your career path, get accustomed to always defining your domain before you begin.

Big Data or Small Data? The Correct Answer is Both

In this special guest feature, Dr. Ricardo Baeza-Yates, CTO at NTENT, discusses how it’s not enough to weigh data decisions on the descriptor of big versus small alone – a number of other things must be considered.

The Difference Between Data Science and Data Analytics

In this contributed article, tech writer Rick Delgado, examines the differences between the terms: data science and data analytics, where people working in the tech field or other related industries probably hear these terms all the time, often interchangeably. Although they may sound similar, the terms are often quite different and have differing implications for business.

The Exponential Growth of Data

This is the first entry in an insideBIGDATA series that explores the intelligent use of big data on an industrial scale. This series, compiled in a complete Guide, also covers the changing data landscape and realizing a scalable data lake, as well as offerings from HPE for big data analytics. The first entry is focused on the recent exponential growth of data.

Learn Data Science: Eight (Easy) Steps

Our friends over at DataCamp have produced the “Become a Data Scientist in 8 Steps” infographic providing a view of the eight steps that you need to to through to learn data science. Some of these eight steps will be easier for some than for others, depending on background and personal experience, among other factors.

How Predictive Analytics is Changing the Retail Industry

In this special guest feature, Dean Abbott of SmarterHQ discusses how data science and predictive modeling have become the holy grail for the retail industry.