Tag Archives: data science

Deep Learning: New Kid on the Supervised Machine Learning Block

June 27, 2017

In the second instalment of this blog, we introduced machine learning as a subfield of artificial intelligence (AI) that is concerned with methods and algorithms that allow machines to improve themselves and to learn from the past. Machine learning is often concerned with making so-called “supervised predictions,” or learning from a training set of historical… Read More »

Data Science Versus Data Engineering

June 6, 2017

In the third instalment of this blog, we told you that “the analytic discovery process has more in common with research and development (R&D) than with software engineering.” But – symmetrically, if confusingly – what comes after the discovery process typically has more in common with software engineering than with R&D. The objective of our… Read More »

Discovery, Truth and Utility: Defining ‘Data Science’

May 16, 2017

Gregory Piatetsky-Shapiro knows a thing or two about extracting insight from data. He co-founded the first Knowledge Discovery and Data Mining workshop in 1989 that we briefly discussed in the second installment of this series of blogs. And he has been practicing and instructing pretty much continuously since then. But what is it, exactly, that… Read More »

Machine Learning Goes Back to the Future

May 9, 2017

In the first installment of this blog series, we described how the quest for artificial intelligence (AI) gave us the discipline of machine learning – the study of how to enable an intelligent agent to learn from data to improve its performance. But what has any of that got to do with commercial analytics? Learning… Read More »

DevOps For Data Science: Why Analytics Ops Is Key To Value

December 13, 2016

It may be a stretch to call data science commonplace, but the question “what’s next” is often heard with regard to analytics. And then the conversation often turns straight to Artificial Intelligence and deep learning. Instead, a tough love review of the current reality may be in order. The simple truth is that, as currently… Read More »

The Joy of Data Viz: The Data You Weren’t Looking For

December 8, 2015

“A good sketch is better than a long speech” – Napoléon Bonaparte I recently came across this quote on the opening page of Phil Simons’ book,  “The Visual Organization, Data Visualization, Big Data, and the Quest for Better Decisions.” It is available online. Data visualization, or Data Viz as it is often referred to, is… Read More »

Data Modeling Requires Detailed Mapping — Learn Why

November 30, 2015

Mapping is an important step to understanding your data and where the data resides in your ecosystem. Mapping takes us from the known to the unknown and is effectively accomplished by using mapping tools, adopting best practices, and having a common understanding of how the mappings will be used. But mapping does take a considerable amount… Read More »

Pluralism and Secularity In a Big Data Ecosystem

August 25, 2015

Solutions around today’s analytic ecosystem are too technically driven without focusing on business values. The buzzwords seem to over-compensate the reality of implementation and cost of ownership. I challenge you to view your analytic architecture using pluralism and secularity. Without such a view of this world your resume will fill out nicely but your business… Read More »

Optimization in Data Modeling 1 – Primary Index Selection

July 14, 2015

In my last blog I spoke about the decisions that must be made when transforming an Industry Data Model (iDM) from Logical Data Model (LDM) to an implementable Physical Data Model (PDM). However, being able to generate DDL (Data Definition Language) that will run on a Teradata platform is not enough – you also want… Read More »

Why We Love Presto

June 24, 2015

Concurrent with acquiring Hadoop companies Hadapt and Revelytix last year, Teradata opened the Teradata Center for Hadoop in Boston. Teradata recently announced that a major new initiative of this Hadoop development center will include open-source contributions to a distributed SQL query engine called Presto. Presto was originally developed at Facebook, and is designed to run… Read More »