Inside Big Data White Papers

Python stands out as the language best suited for all areas of the data science and machine learning framework. Designed as a flexible general purpose language, Python is widely used by programmers and easily learnt by statisticians. Download the new guide from ActiveState that provides a summary of Python’s attributes, as well as considerations for implementing the programming language to drive new insights and innovation from big data. Read More »

The value and benefits of a data catalog are often described as the ability for analysts to find the data they need quickly and efficiently. Data cataloging accelerates analysis by minimizing the time and effort that analysts spend finding and preparing data. Read More »

Using high performance parallel storage solutions, geologists and researchers can now incorporate larger data sets and execute more seismic and reservoir simulations faster than ever before, enabling higher fidelity geological analysis and significantly reduced exploration risk. To lean more download this white paper. Read More »

As we think about the entire analytics life cycle, it’s important to consider data preparation, deployment, performance, scalability and governance, in addition to algorithms. Download the new e-book explores how organizations are using open source analytics and SAS, and what role SAS plays throughout the analytics life cycle. Read More »

In visual search is seen as the next great search frontier, and Microsoft’s Bing has tapped the power of NVIDIA GPUs to make it a reality. Download the new white paper from NVIDIA to explore how Bing deployed NVIDIA technology to speed up object detection and deliver pertinent results in real time. Read More »

In this guide we explain the difference between AI, machine learning and deep learning, and includes highlights of the insideBIGDATA audience survey. To learn more about AI and deep learning download this guide. Read More »