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Building a Winning Data Science Team

In this contributed article, Brad Cordova, co-founder and CTO of TrueMotion, discusses the importance of building a winning data science team, including actionable tips drawn from his own experience on structure, investment and building a culture where data science thrives.

When Data Quality meets Context, Health Wearables Find Their Power

In this special guest feature, Jiang Li, CEO of VivaLNK, offers three key considerations when it comes to remote patient monitoring (RPM) and the Internet of Healthcare Things (IoHT); a world where devices remotely and continuous gather meaningful data on the state of our health before, during and after a health event.

How Operational Machine Learning is Transforming Industrial Operations

Our friends over at Falkonry just released the new infographic below “How Operational Machine Learning is Transforming Industrial Operations.” It includes some great data on how fast the industry is growing, who is using it (Toyota, Ciner, Honda, Kawasaki, etc.), how predictive analysis works, and applications per market (semiconductor, oil and gas, energy, automotive, mining, etc.).

Citizen Data Scientists – Are we there yet?

In this contributed article, Matthew Attwell, Risk & Client Services Director at The ai Corporation (ai), discusses the advent of the Citizen Data Scientist and how this designation is unfolding over time. Undoubtedly in the long term, solutions will become more flexible and dynamic to realize the full definition of the CDS. In the short term, however, we require data scientists to actively engage with and support the budding CDS within the business.

Looker Enhances Data Science Capability with Integration for Google Cloud BigQuery ML

Looker, a leading data platform company, announced an integration with Google Cloud BigQuery ML (BQML) that reduces the time-to-value of data science workflows and allows business users to operationalize insights with interactive predictive metrics.

Best of insideBIGDATA Technology Guides

Over the years since I became Managing Editor for insideBIGDATA, we’ve had some amazing partner firms contract with us to write custom technology guides, white papers, and advertorials. In many cases, I authored the materials myself and had a great time working on the project and getting to know the client subject matter experts. These resources consistently rank high every month in our vast library of content. In this article I wanted to provide an easy access list of the top 10 white papers of all time. The themes cut across the most popular in the big data industry. Enjoy!

Big Data Day LA 2018

Get ready for the local big data event of the year! The 2018 edition of Big Data Day LA is taking place on Saturday, August 11, 2018 at the University of Southern California. This full-day conference is a great way to immerse yourself in everything data with sessions tracks on diverse topics: Data, AI/ML/Data Science, Emerging Tech, Infrastructure and Security, and Visualizations/UI/Use Cases.

Data Science at Microsoft – Interviews with Practitioners

In this technical brief I wanted to pass along some great resources in support how data scientists approach their profession and illustrate the kind of background a typical data scientist might have to become successful. insideBIGDATA previously featured four compelling podcast interviews with Microsoft data scientists.

Best of for AI, Machine Learning, and Deep Learning – June 2018

In this recurring monthly feature, we will filter all the recent research papers appearing in the preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

insideBIGDATA “Ask a Data Scientist” Series

Welcome to the series of articles sponsored by Intel – “Ask a Data Scientist” from insideBIGDATA’s popular Data Science 101 channel. These articles constitute many of our site’s most popular resources for newbie data scientists. The 12 articles listed below were from reader submitted questions of varying levels of technical detail and answered by a practicing data scientist – sometimes by me and other times by an Intel data scientist.