Tag Archives: data science

Building Trust in AI: How to Get Buy in for the Black Box

November 21, 2017

Maybe it’s something innate to human nature, or maybe we’ve all seen one too many sci-fi movies (I’m looking at you Hal and 2001: A Space Odyssey), but people tend to view new technology skeptically. This is especially true when it comes to technology that makes recommendations or tells us how to do something. A… Read More »

The New Wave of Machine Learning

October 12, 2017

In the last few years, analytics has evolved significantly. We’ve moved from the adoption of big data technology that was all about Hadoop and Spark, to an increased focus on machine learning algorithms, deep learning and artificial intelligence (AI). In the last year alone, I’ve noted a renewed hype around machine learning, which together with the… Read More »

Is failure good for your data scientists?

September 25, 2017

If you’ve heard of data science (if you haven’t, where have you been and how did you find this blog?), you’ve probably heard of “fail fast”. The fail fast mentality is based on the notion that if an activity isn’t going to work, you should find out as quickly as possible, and stop doing it.… Read More »

Occam’s razor and machine learning

September 12, 2017

In the last instalment of this blog series, we discussed objectives and accuracy in machine learning. And we described two crucial tests for the utility of a machine learning model: The model must be sufficiently accurate and we must be able to deploy the model so that it can produce actionable outputs from the available… Read More »

Objectives and accuracy in machine learning

September 7, 2017

We get to go to a lot of conferences. And we’re always amazed at how many vendors and commentators stand up at events and trade shows and say things like, “The objective of analytics is to discover new insight about the business”. Let us be very clear. If the only thing that your analytic project… Read More »

Is analytics operations the key to successful data science?

September 1, 2017

Despite a continuing shortage of data science skills, data-driven teams do exist in businesses across many industries. Expectations are high and the promises of predictive analytics, prescriptive analytics and artificial intelligence (AI) has captured the imagination of many. Now that the role, the skill-sets and the responsibilities of data science are becoming better defined, how… Read More »

Five ways Analytics and Data Science can add business value

August 30, 2017

The conversation around big data has grown, well, big in recent times. So much so that it is now part of the day to day vernacular for businesses around the world. Nowhere is this more prevalent than in the thriving technology ecosystem happening right now in the UK. Any organisation can leverage the exponential data… Read More »

The future of marketing: Q&A with Andrew Stephen and Yasmeen Ahmad

August 9, 2017

Recently, Professor Andrew Stephen from Saïd Business School at Oxford University spoke to Yasmeen Ahmad about her thoughts on the future of marketing in an increasingly digitised world, including the key challenges facing marketers today. See the Q&A below: Stephen: What are the biggest challenges that marketers are facing right now, particularly with respect to… Read More »

Ready for an Instant Education in Data Science? Get It at Teradata PARTNERS

August 8, 2017

More than ever, “WOW!” business outcomes are driven by data scientists, and there are many types of data scientist. Everyone can benefit from understanding the ways data scientists explore, analyze and execute strategic business decisions and plans. The 2017 Teradata PARTNERS conference offers many sessions focused on Data Science – so get an instant education… Read More »

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 »