Through this series of blog posts, you will gain a solid overview of the fundamental steps involved in almost all data science projects. These basic processes will help you assess the context, as well as constraints, for selecting suitable algorithms to use in a project. There are four key steps in a data science project. […]
Since about 2010, “Big Data” has become the ubiquitous term to describe all the data that is generated by people from their smartphones, web browsing history, social media and purchasing behaviour, together with any other information that organizations hold about them. Why is big data different to any other type of data? In one sense, […]
Data scientists take the recommendations that the business analysts make and do a variety of tasks including the following: Build the technical case. They apply advanced math and statistics to build the technical cases around the hypotheses that the business analysts build. Data scientists are tasked with building the models required to test these theories. […]
Big data projects originate with solving problems with some business objective in mind. Much of the focus today centers around technology implementation, visualization tools, and data products, but it’s important to remember that technology with no end in mind has little business value. Enter the role of the business analyst. Some people claim that this […]
Before the cool algorithms and complex data architectures, comes the process that all data science projects must follow (I know this already sounds a little bookish, but trust me – I am just breaking the subject down into little pieces!). Different projects follow different approaches, but the objective remains largely the same – let the […]
There is definitely no short supply of data – in fact, there is probably an excess of data available today when accounting for traffic running through social media, transactions, real-time market feeds, and other places. This means there are explosive amounts of data available for the financial sector. The variety of data available and the […]
Humans try to understand the world around them by representing it in different ways. Architects capture attributes of buildings through blueprints and three-dimensional, scaled-down versions. Molecular biologists capture protein structure with three-dimensional visualizations of the connections between amino acids. Statisticians and data scientists capture the uncertainty and randomness of data-generating processes with mathematical functions that […]
The life of a big data consultant can take many different turns. If you’re interested in pursuing this path, make sure you understand how consultants fit into an organization’s strategy from both top-down planning to bottom-up execution. Consulting careers have the potential to be rewarding, both financially and intellectually. But that high reward comes with […]
There’s nothing new in brands using analytics to improve their process and increase their gains. But the scale of the data they use to do this has increased tremendously over the last few years. That’s why you need to read how the following brands leveraged data science to achieve their business goals: They are an […]
In this blog post, I will explain how machine learning fits into the broader landscape of data and computer science. This means understanding how machine learning interrelates with parent fields and sister disciplines. This is important, as these are the terms you will see time and again when searching for relevant study materials and hear […]
