I was recently speaking to some data science friends on Slack, and we were discussing projects and war stories. Something that came across was that ‘data science’ projects aren’t always successful. Somewhere around this discussion a lightbulb went off in my head about some of the problems we have with embarking on data science projects.… Continue reading One weird tip to improve the success of Data Science projects
Recently I’ve been speaking to a number of data scientists about the challenges of adding value to companies. This isn’t an argument that data science doesn’t have positive ROI, but that there needs to be an understanding of the ‘team sport’ and organisational maturity to take advantage of these skills. The biggest anti-pattern I’ve experienced… Continue reading Avoiding being a ‘trophy’ data scientist
I interviewed the interesting and fascinating Ian Wong – he’s the technical co-founder of OpenDoor, which I personally think is amazing as a concept! 1. What project have you worked on do you wish you could go back to, and do better? Pretty much any project I’ve worked on in the past Two projects stick… Continue reading Interview with a Data Scientist – Ian Wong of OpenDoor
I’m delighted to feature my friend Mick Cooney here as an interviewee. Mick has many years of experience in Finance and more recently in Insurance, he co-ran the Dublin R meetup which was very successful and helped foster a data science community in Dublin. More recently he’s been working over in London at an Actuarial… Continue reading Interview with a Data Scientist: Mick Cooney
I’ve been in the Data Science space for a number of years now, I first got interested in AI/Machine Learning in 2009 and have a background typical of a number of people in my field – I come from Physics and Mathematics. One trend I’ve run into both at Corporates and Startups is that there… Continue reading Building Full-Stack Vertical Data Products
I’ve been using Python for a number of years now – but like most things I didn’t really understand this until I investigated it. Firstly let’s introduce what a module is, this is one of Python’s main abstraction layers, and probably the most natural one. Abstraction layers allow a programmer to separate code into parts… Continue reading What happens when you import modules in Python
I recently was experimenting with RNN’s in Keras. I used the example and edited it slightly. This is what I got for Nietzsche – as you can see the answer above to my question is No. ——– diversity: 0.2 ——- Generating with seed: “iginal text, homo natura; to bring it ab” iginal text, homo natura;… Continue reading Are RNN’s ready to replace journalists?
The insightful Data Scientist Trey Causey talks about Software Development Skills for Data Scientists I’m going to write about my views on Code Review – as a Data Scientist with a few years experience, and experience delivering Data Products at organizations of varying sizes. I’m not perfect and I’m still maturing as an Engineer. A good… Continue reading Why Code review? Or why should I care as a data scientist.
I’ve been doing Data Science projects, delivering software and doing Mathematical modelling for nearly 7 years (if you include grad school). I really don’t know everything, but these are a few things I’ve learned. Consider this like a ‘joel test‘ for Data Science. Use a reproducible framework like Cookiecutter Data Science. My workflow used to… Continue reading 3 tips for successful Data Science Projects
Jessica Graves is a Data Scientist who currently works on fashion problems in New York City. She’s worked with Hilary Mason at Fast Forward Labs and keeps in regular contact with the London startup scene. After many months of asking her for an interview she finally gave in – and she shares her unique perspective… Continue reading Interview with a Data Scientist – Jessica Graves