Click to learn more about author Steve Miller. This is the Second Part of a two-part series. Read Part One here. About ten years ago, a software vendor with whom my intelligence consulting company partnered conducted a survey on the usage of various analytics platforms that were then prominent in the space. As a proselyte […]
The 80/20 Challenge: From Classic to Innovative Data Science Projects
Click to learn more about author Rosaria Silipo. Sometimes when you talk to data scientists, you get this vibe as if you’re talking to priests of an ancient religion. Obscure formulas, complex algorithms, a slang for the initiated, and on top of that, some new required script. If you get these vibes for all projects, […]
How to Tackle Social Issues with AI?
Click to learn more about author Charles Richard. Never have disruptive technologies like artificial intelligence (AI) and machine learning had such an impact on how your business interacts with your user community as it does now. And there is a reason for that; the accessibility of computing power and data sets have ushered in breakthroughs […]
How Interactive Technology is Revolutionizing Data Analysis in 2019
Click to learn more about author Pippa Edelen. The 21st century has seen some big developments in data analysis. It’s seen: The birth of the cloud – arguably in 2000 The development of Big Data – in 2005 The creation of technologies such as Hadoop (2006) and Spark (2014) that allowed computation on enormous data […]
Simplistic and Credulous Data Science: Part One
Click to learn more about author Steve Miller. A few months ago, I was solicited by one of my many LinkedIn Data Science groups to participate in a survey. I’m generally a bit suspicious of surveys, reviewing the questions carefully before making the decision to respond. This initiative, though, posed a question that quite intrigued […]
Automated Machine Learning: Just How Much?
Click to learn more about author Rosaria Silipo. An interview with three data scientists and guided automation experts There is currently a lot of talk about automated machine learning. There is also a high level of skepticism. I am here with data scientists Paolo Tamagnini, Simon Schmid and Christian Dietz.. to ask a few questions […]
The Data Quality Dimension “Coverage” is the Most Prominent for AI Outcomes
Click to learn more about author Tejasvi Addagada. The first Data Quality challenge is most often the acquisition of right data for Machine Learning Enterprise Use cases. Wrong Data – Even though the business objective is clear, data scientists may not be able to find the right data to use as inputs to the ML service/algorithm […]
What is Augmented Data Science and Why is it Important to My Business?
Click to learn more about author Kartik Patel. If Data Science was once the sole domain of analysts and data scientists, Augmented Data Science represents the democratized view of this domain. With Augmented Data Science, the average business user can engage with advanced analytics tools that allow for Automated Machine Learning (AutoML) and leverage sophisticated analytical […]
Leveraging Natural Language Processing Within a CDP
Click to learn more about author Jonathan Lee. In my last column for DATAVERSITY I sought to define the concept of a Customer Data Platform (CDP), noting that a CDP “creates a comprehensive view of each customer by capturing data from multiple systems.” What I didn’t do however, was to delve into a discussion of […]
Nowcasting Chicago Crime with R
Click to learn more about author Steve Miller. In my many years as a data scientist, I’ve spent more time doing forecast work than any other type of predictive modeling. Often as not, the work’s involved forecasting demand for an organization’s many products/lines of business a year or more out based on five or more […]