Data mining Archives - Smart Vision Europe

What is the difference between data science and machine learning?

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. This post will dive deeper into the nuances of each field. What is data science? Data science is a broad, multidisciplinary field that extracts value from today’s …

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What skills are needed to successfully run data science projects in the cloud?

By Berni Simmons

In my most recent blog post I wrote about the benefits and opportunities that cloud computing brings to data science projects and reciprocally, how data science projects provide a great way to demonstrate the value of cloud computing. In this post I want to discuss the cloud computing skills challenge and provide some advice on …

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Three questions to ask when reading articles about artificial intelligence

By Jarlath Quinn

You may have noticed by now that there seem to be a couple of recurring themes in the plethora of articles and news programmes about artificial intelligence (AI). These themes can be summed up as a) “The dangers of AI” and b) “The limitations of AI”. Articles addressing the dangers of AI tend to focus …

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Thinking of using spreadsheets for advanced analytics? Think again.

By Rachel Clinton

When we’re talking to potential clients about advanced analytics we often ask them what tools they’re currently using. More often than not they say they’re using spreadsheets. Spreadsheets are one of the most widely used tools for statistical analysis and of course, most businesses couldn’t run without them. However, when it comes to advanced analytics …

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Fear and loathing in machine learning

By Jarlath Quinn

Over the past two years I’ve noticed a steady stream of articles in the mainstream press and business journals centred on the themes of a) the dangers of machine learning 1 2 or b) the limitations of machine learning 3 4. Many of these articles refer to incidents where machine learning initiatives have echoed and …

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Which data science tools should you learn?

By Rachel Clinton

I’ve blogged several times now about different aspects of data science. A conversation I’ve been having more and more frequently now is about what tools people should learn if they’re hoping to develop a career in data science. Obviously there are many different factors to be taken into account here. You’ll want to think about …

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Data science is everywhere, so why no data scientists to be seen?

By Rachel Clinton

Data science is everywhere at the moment. Nearly as everywhere as big data, but not quite. Books out there are making the concepts behind statistics and predictive analytics more and more accessible not only to those in business making decisions everyday but also to the average man or woman on the street. Try Super Crunchers …

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Understanding what drives your net promoter score – how data science can help

By Jarlath Quinn

The concept of the net promoter score was introduced to the world in Frederick Reichheld’s seminal Harvard Business Review article The One Number You Need to Grow in 2003. Reichheld’s research led him to believe that there was a deep and intrinsic link between profitable growth and customer loyalty. Two years of research revealed that, for most industries, …

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Nine tips for effective data mining

By Rachel Clinton

In my career I’ve seen many examples of successful and unsuccessful data mining projects. I’m often asked how clients can maximise the chances of their project being successful and, based on the many projects I’ve been involved with over the years, I think there are 9 things that really help. When these factors are in …

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A first look at IBM’s Watson Analytics

By John McConnell

An analytical tool for the business user. It isn’t a new idea, more a nut that many of us have been trying to crack for years. Today, of course, this problem is more pressing than ever. There are more questions that can be answered with analytics, more data to analyse and fewer trained analysts to …

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Predictive analytics – five ways your business can benefit

By Rachel Clinton

When talking to companies who have yet to invest in predictive analytics I am often asked if I am sure that they will benefit. Will it be worth the time and effort involved in setting up a predictive analytics project? What kinds of benefits can they realistically expect? Are they large enough to reap the …

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Do you have to be a data scientist to do predictive analytics?

By Rachel Clinton

What is a data scientist? The predictive analytics field seems to love nothing more than giving a new name to an established concept. In my last post I argued that the concept of ‘big data’ itself is nothing new. Over the last few years I’ve seen more and more job ads recruiting ‘data scientists’ and I find myself rather unjustifiably irritated …

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Predictive analytics – how much data do you really need?

By Berni Simmons

When I’m talking to prospective clients something I hear a lot is ‘but we don’t really have enough data to do any data mining’. It’s a common misconception that you need vast terabytes of data to be able to do anything meaningful in terms of analytics. In fact there are a number of similar misconceptions …

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Predictive analytics: how small improvements can deliver big results

By Berni Simmons

It never ceases to surprise me at the wide array of interesting and smart folks we have the privilege to meet through the course of our work at Smart Vision Europe. We are two months into 2014 and I have been checking back on my diary to make sure there are no loose ends. I’m …

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Five myths about big data

By Rachel Clinton

Big data is everywhere at the moment. There’s a lot of talk about it, much of which presents big data as a problem to be solved rather than as an opportunity to be seized. There are many organisations that could be using big data for powerful predictive analytics but aren’t, because they’ve bought into one …

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Do you really need special software for predictive analytics?

By Rachel Clinton

When I’m talking to prospective clients, one of the questions I’m regularly asked is why they should invest in expensive predictive analytics software when they already have Excel. Excel has some useful features and for many organisations it can be a useful toe in the water of analytics but it’s really not built for the …

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