by Angela Guess John Weathington recently wrote in TechRepublic, “There’s no reason for a data scientist to don a white coat or play with beakers. In practice they should be an integral part of your corporate strategy, driving forward the development of your next innovation. The idea of stashing data scientists away in a data […]
Vetting the Actual Science Behind Data Science
Click here to learn more about author James Kobielus. Everybody wants to rule the world–or, at the very least, discover the fundamental rules that rule the world. That’s why we have scientists. Statistical models are the heart of most scientific inquiry. In business applications, for example, data scientists often work with behavioral data that is […]
Data Science, Witchcraft, and Cookbooks
by Angela Guess Yasmeen Ahmad of Teradata recently wrote in Forbes, “Trying to explain what I do to friends and family can be difficult. They’re intrigued by the title. Data Scientist. But ‘what does it mean, exactly?’ Invariably, the mystery deepens as puzzlers wrestle with the idea of a data and science mash-up. And as […]
The Business Benefits of Deep Learning
Over the past several years, the global Data Science community has watched the rise and steady penetration of such concepts as neural networks, Deep Learning, and back propagation. As research in Artificial Intelligence continues to evolve, an increasing interest in Neural Networks and Deep Learning has caught the fancy of worldwide business owners and operators. […]
No Relief with Hadoop – Managing The Big Data Reality Gap
Click here to learn more about author Jon Bock. There has been much anticipation that businesses would find relief for their analytics headaches in Hadoop, the open source software for distributed processing and distributed storage of large data sets across clusters of commodity or cloud hardware. There is no doubt Hadoop systems can handle large […]
The Vocabulary of Data Science: A Guide
by Angela Guess David Kil and Mark Killiron recently broke down the vocabulary of data science on EdSurge.com for students, non-data professionals, and anyone interested in learning more about this burgeoning field. Their list of terms includes, “DESCRIPTIVE ANALYTICS: Examines historical data and identifies trends or patterns over time from known facts to inform future […]
Putting Notebooks to Work in Data Science
by Angela Guess Dan Osipov recently wrote in Datanami, “Interactive notebooks are experiencing a rise in popularity. How do we know? They’re replacing PowerPoint in presentations, shared around organizations, and they’re even taking workload away from BI suites (more on that later). Even though they’ve become prominent in the past few years, they have a […]
Enterprise Data World 2016: 8 Data-Driven Takeaways
How do you transform your enterprise from being data-incompetent to data-driven? How do you incorporate and leverage your legacy data assets with your latest Big Data assets? How do you successfully implement Data Quality, Data Governance, and Master Data Management into your existing business and IT structures without causing undue chaos or friction? How do […]
Web Scraping for Data Science — Part 2
Click here to learn more about author Steve Miller. Read Part 1 of this blog series here. Between R and Python, analytics pros are covered on most data science bases R-Python. In last month’s blog, I discussed simple webscraping using Python in a Jupyter notebbok, the nifty css-generating tool SelectorGadget, and the Python XML and HTML handling package lxml. […]
The API Economy: A Big Ball of CRUD
Click this link to learn more about the author Dave Duggal. Quote: “The use of APIs has exploded with the growth of distributed computing, driven by the popularity of the Web, Cloud and now, the Internet of Things (IoT)” Back in 1999 an academic paper, “The Big Ball of Mud” exposed fundamental limitations of ‘modern’ software […]