This week I’ve been programming in JavaScript, something of a novelty for me. Jealous of the Dear Leader’s automatically summarize tool I wanted to make something myself, hopefully a future post will describe my timeline visualising tool. Further motivations are that web scraping requires some knowledge of JavaScript since it is a key browser technology […]
You will have noticed some activity over the past few weeks, as we have begun reaching out about the new ScraperWiki platform. We’ve blogged about some of the new features, and have invited the first ever users outside the office to have a poke around the beta. That initial feedback has been immeasurably helpful, and […]
Next week, a plethora of organisations, hackers and data scientists are celebrating “Big Data Week,” and the ScraperWiki team will be taking part in London. We will be supporting the DoES Liverpool exhibit at the Internet of Things stream of Internet World at Earls Court (#internetworld2013). Francis will also be giving a talk at 1:30 […]
Hi! I’m the latest member of ScraperWiki, joining the Data Science team this week. Data Science is a fascinating new direction for me, being “officially” an Electronic Engineer. I’ve spent the last couple of years in a large company hammering out fast C++ and trying (unsuccessfully) to convert everyone to Python. But what really excites […]
Machine learning is about prediction, and prediction is a valuable commodity. This sounds pretty cool and definitely the sort of thing a data scientist should be into, so I picked up Machine Learning in Action by Peter Harrington to get an overview of the area. Amongst the examples covered in this book are: Given that […]
Late at night, after a long conversation in a bar (after Social Media Cafe), Zach mentioned one feature that everyone loved about Kasabi. It had an overview page, which automatically summarised each dataset. Of course, Kasabi did it using linked data – telling you how many of your triples were geographic locations, and how many […]
The vast majority of my contributions to the web have been loosely encoded in the varyingly standard-compliant family of languages called English. It’s a powerful language for expressing meaning, but the inference engines needed to parse it are pretty complex, staggeringly ancient, yet cutting edge (i.e. brains). We tend to think about data a lot […]
My next review is of Andy Kirk’s book Data Visualization: a successful design process. Those of you on Twitter might know him as @visualisingdata, where you can follow his progress around the world as he delivers training. He also blogs at Visualising Data. Previously in this area, I’ve read Tufte’s book The Visual Display of […]
This is a review of Robert I. Kabacoff’s book R in Action which is a guided tour around the statistical computing package, R. My reasons for reading this book were two-fold: firstly, I’m interested in using R for statistical analysis and visualisation. Previously I’ve used Matlab for this type of work, but R is growing in […]
As a business person, if I want insight into my business needs, I can ask a data scientist for answers, so I can make better decisions! Urgh: the sound I make reading that. I am starting to wonder if Data Science is seen as magic, and insight as arcane wisdom distilled from eyes of newts […]
