Science progresses when researchers build on prior work to extend, test, and apply theories. Aggregating the quantitative findings from prior research – meta-analysis — plays a significant role in advancing science, however current techniques have limitations. They assume prior studies share similar substantive factors and designs, yet many studies are heterogenous. A new method, co-created by MIT Sloan School of Management Prof. Hazhir Rahmandad, solves this problem by aggregating the results of prior studies with different designs and variables into a single meta-model.
In this contributed article, Jules S. Damji, an Apache Spark Community Evangelist with Databricks, shows how as the value of data continues to grow, the next-generation smart grid should become a reality, benefiting utility companies and consumers alike.
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.
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.”
In this special guest feature, Ubuntu Evangelist Randall Ross writes that the OpenPOWER Foundation is hosting an all-new type of developer event. “The OpenPOWER Foundation envisioned something completely different. In its quest to redefine the typical developer event the Foundation asked a simple question: What if developers at a developer event actually spent their time developing?”
In this contributed article, tech writer Linda Gimmeson goes over a short list of the most popular developer tools for machine learning practitioners including Amazon Machine Learning, Tensor Flow, Azure Machine Learning Studio, H20, Caffe, MLlib, and Torch.
Above the Trend Line: machine learning industry rumor central, is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.
In this contributed article, web content writer Gloria Kopp presents 5 simple ways that big data analytics can make a difference to an e-commerce business enterprise.
In the retail business, big data is poised in the coming years to open up huge opportunities in the way stores (both physical and online) fundamentally operate and serve customers. Given the incredibly small margins, Big Data will also provide much needed efficiency improvements – from tighter supply chain management to more targeted marketing campaigns – that can make a big difference to a retail business of any size.
Harte Hanks Brings Next Generation Data and Analytics to Marketers with Opera Solutions’ Signal Hub Platform
Harte Hanks ( NYSE : HHS ), a leader in customer relationships, experiences, and interaction-led marketing and Opera Solutions, a leader in Data Analytics, announced the market availability of their data and analytic solution delivered through Opera Solutions’ Artificial Intelligence (AI) and Machine Learning platform, Signal Hub™.