A STRING OF MAGIC PONIES
Twitter’s acquisition of Magic Pony (named after one investor’s attempt to analogise how unbelievable their technology is) has been one of the most high profile moves into machine learning so far. As a result, we can reasonably expect our timelines to feature automatic sharpening of low-resolution and blurred video before too long […]Read More
A RESPONSE TO THE REGISTER ON TECH NATION 2016
Over at The Register, Marcus Gibson has written an article about Tech Nation 2016 entitled, Oh TechNation. Britain’s got tech talent. Just not like this – Giving shiny-happy UK ‘digital’ sector survey the digit. Before we get started, Marcus publishes The Gibson Index, which he describes as ‘the world’s first national database of early stage technology […]Read More
FROM PANDAS TO SPARK
Recently I had the opportunity to use Apache Spark as an ETL tool for a project I am working on here at GrowthIntel. It was the first time I was working in a cluster computing framework. The experience was a good although slightly challenging one. A cluster computing framework, also called distributed or parallel computing […]Read More
THREE WAYS TO MARKET WITH MILITARY PRECISION
We’ve just published our latest eBook. It’s about submarines, algorithms and how to scale outbound marketing without compromising on the quality of your leads. As a result, it’s great news for both B2B marketers and their sales teams. Cheat at Battleships: Three Ways to Market With Military Precision Inside: Interesting references to naval sonar and our CTO and co-founder, Prash Majmudar […]Read More
DATA PIPELINES WITH LUIGI, PART 2
In our previous post, we showed how Luigi helps you to build a simple task dependency graph by providing two simple abstractions: a Task class and an output class. We gave the example of having a regularly updated count of all the companies in the UK. Having a count of all the companies in the […]Read More
DATA PIPELINES WITH LUIGI, PART 1
This introduction to Luigi was originally presented as a talk at PyData London 2015. Feel free to check out the slides! At Growth Intelligence, we use predictive modeling to help generate high quality leads for our customers. We’re essentially helping them answer this question: where should I focus my outbound sales and marketing efforts to […]Read More
A VIEW FROM INSIDE THE DEMAND SPHERE: AGILE DATA SCIENCE
For third post in our View from Inside the Demand Sphere series I thought we would step away from the nitty gritty technical issues a bit and take a look at what appears to be an open question across the data science community: what is the best way to manage a data science team? There […]Read More
A VIEW FROM INSIDE THE DEMAND SPHERE: EXPLORATORY DATA ANALYSIS IN PANDAS WITH MASKS
Often at GrowthIntel when we receive a list of prospects from a new client the first thing we do, once it has been matched against our datasets, is some exploratory data analysis. We’ve found that iPython Notebook (or rather Jupyter Notebook) combined with pandas and Matplotlib is an excellent combination which allows us to slice, […]Read More
A VIEW FROM INSIDE THE DEMAND SPHERE: BUILDING PREDICTIVE MODELS WITH ELASTICSEARCH
Welcome to the first of a regular series of posts from the technical team at GrowthIntel. We have called this blog ‘A View from Inside the Demand Sphere’ and in it we are going to explore a range of technical issues, across both Data Science and engineering, to give a bit of insight into how […]Read More