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How Data Science Can Save the Traditional Banking Industry

In today’s technologically advancing world, traditional banking groups are being seriously challenged. As Google, Amazon, Facebook, Apple offer more and more banking services and financial technology startups gain traction, the banking industry must take a look at how it can stay competitive. To do this, banking needs to rely on data science.

New Blue Yonder Solution Helps Grocers Optimize Replenishment for Fresh Assortments

Set against a backdrop of declining profitability and significant changes in consumer lifestyles, retailers are under pressure to deliver the best freshness and optimal availability to their customers without the ability manage cost to serve. Typically, 40 percent of grocery revenue is driven by fresh, according to the latest McKinsey report: get this right and the rest will follow, including the customer and profits. Grocery has traditionally struggled to deliver the right customer experience in fresh without forsaking margin.

The Math Behind Basketball’s Wildest Moves

If basketball is of more interest to you than business intelligence, you’ll like this TED talk by Rajiv Maheswaran. If you want pithy examples of what algorithms and machine learning are, you’ll like Maheswaran’s talk even more. Algorithms are necessary to the functioning of any BI software, and machine learning has been called “the new BI.” Googling those terms is useful, but a little dull.

It’s Time to Redesign Medical Data

In the TEDMED talk below, Thomas Goetz looks at medical data, making a bold call to redesign it and get more insight from it. Your medical chart: it’s hard to access, impossible to read — and full of information that could make you healthier if you just knew how to use it.

Big Data and Healthcare

In this contributed article, Jaspinder Grewal, current CEO of CareSkore, discussed why it’s becoming more difficult for doctors to focus solely on their patients. While the rise of the health IT landscape was meant to automate certain tasks, the reality is it’s taking up more and more of a doctor’s time.

The Games Industry’s Journey Into Deep-Data

In this special guest feature, Mark Robinson, CEO of deltaDNA, looks at the challenges solved by big data in the games industry and the evolution of analytics which has enabled these changes.

Case Studies: Big Data and Healthcare & Life Sciences

The insideBIGDATA Guide to Healthcare & Life Sciences is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting new area of technology. This segment focuses on big data case studies.

Data Analytics, Machine Learning, and HPC in Today’s Changing Application Environment

In this video from the Intel HPC Developer Conference, Franz Kiraly from Imperial College London and the Alan Turing Institute describes why many companies and organizations are beginning to scope their potential for applying rigorous quantitative methodology and machine learning.

Amplitude Study Finds that “N Day” Retention Metric Underestimates the Percentage of Users who Return to Apps by a Factor of 3.5X

Amplitude released findings from a study of retention data collected from its popular analytics platform and found that the most commonly used retention metric, known as ‘N Day,’ underestimates the percentage of users who return to apps over time by a factor of 3.5x.

Ohio State Launches High-Performance Deep Learning Project

Deep learning is one of the hottest topics at SC16. Now, DK Panda and his team at Ohio State University have announced an exciting new High-Performance Deep Learning project that aims to bring HPC technologies to the DL field. “Welcome to the High-Performance Deep Learning project created by the Network-Based Computing Laboratory of The Ohio State University. Availability of large data sets like ImageNet and massively parallel computation support in modern HPC devices like NVIDIA GPUs have fueled a renewed interest in Deep Learning (DL) algorithms. This has triggered the development of DL frameworks like Caffe, Torch, TensorFlow, and CNTK. However, most DL frameworks have been limited to a single node. The objective of the HiDL project is to exploit modern HPC technologies and solutions to scale out and accelerate DL frameworks.”