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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.

7 Steps From Raw Data to Insight

Data scientists generally ascribe to the “machine learning process” which is seen as a roadmap to follow when working on a data science project. The infographic at the end of this article provides a detailed work flow that it is general enough to encompass pretty much any data science project.

TickSmith Releases a Python Tool for the New Generation of Financial Data Scientists

TickSmith, a leader in Big Data applications, released an open-source Python API feature to obtain data from its flagship TickVault big data platform.  Based on Hadoop technology, TickVault processes, stores, and analyzes massive amounts of capital market data. The addition of the Python API  toolkit to TickVault provides data scientists fine-grained access to historical exchange […]

Interview: Jennifer Marsman, Principal Software Development Engineer at Microsoft

In this podcast interview, I caught up with Jennifer Marsman, Principal Software Development Engineer at Microsoft, to find out what it’s like to be a data scientist at Microsoft and get her take on the upward trajectory of AI and deep learning that we’re seeing in the industry today.

DataScience.com Rolls Out Extensive Update to Enterprise-Ready Data Science Platform

DataScience.com has rolled out extensive new features to accommodate the requirements of enterprise data science teams including deployment of  the platform on-premise and across multiple public cloud providers. The latest release expands enterprise support for data scientist’s tools of choice,  such as Jupyter and RStudio, in addition to support for Python, R, Spark, and SAS. […]

New Research Proves Increased Awareness in the Value of Open Data Science, but Enterprises are Slow to Respond

New research announced by Continuum Analytics, the creator and driving force behind Anaconda, a leading Open Data Science platform powered by Python, finds that 96 percent of data science and analytics decision makers agree that data science is critical to the success of their business, yet a whopping 22 percent are failing to make full use of the data available. These findings are included in Continuum Analytics’ new eBook, Winning at Data Science: How Teamwork Leads to Victory.

Winning at Data Science: How Teamwork Leads to Victory

Big Data … best defined as extremely large data sets that may be analyzed computationally to reveal patterns, trends and associations, in everything  ranging from human behavior to scientific research to sensor data, has grown exponentially since the early 1990s. The increase in Big Data collection  has forced enterprises to capitalize on these massive data […]

Open Source Tools for Enterprise Data Science

Learn about open source tools for enterprise data science by downloading the new white paper, “DataScience Trends Report: Open Source Tools for Enterprise Data Science” by DataScience, Inc. The white paper looks at activity data from the most popular GitHub repositories to identify trends in data visualization tools, deep learning libraries, and open source licensing using the interactive DataScience Trends tool.

Open Source Tools for Enterprise Data Science

Proprietary solutions, once the mainstay of enterprise data science, are now being eclipsed by open source projects like R, Spark, and TensorFlow. There are several reasons for this trend: Open source tools offer endless opportunities for collaboration and contribution, and many have been built out to the point that they provide very real value, even at […]

Should You Use Python or R for Your Programming Language?

In this contributed article, technology writer and blogger Kayla Matthews discusses the age-old “R vs. Python” debate that has circulated around in the data science community for the past few years. “When it comes to choosing a programming language, there really are only two choices if you’re working with data. For data science, machine learning, statistics, IoT technology and even automation, the two best languages to use are Python and R.”