It gives me great pleasure to interview Vicki Boykis – we’ve chatted a lot on Twitter over the past few years and her blog/ side projects have been inspiring for my own. Vicki is a Data Scientist and Engineer who tweets awesome stuff. She’s well worth following. Her twitter bio – Born: Jewish in Russia. Raised:… Continue reading Interview with a Data Scientist – Vicky Boykis
TLDR: This is an opinionated post, but based on recent trends. What is Probabilistic Programming? I recently wrote a course teaching this. Probabilistic Programming is a newish paradigm used in Quantitative Finance, Biology, Insurance and Sports Analytics – it allows you to build generative models to infer latent parameters and the uncertainty of those parameters. It’s been… Continue reading Why Probabilistic Programming is the next big thing in Data Science
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
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’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 recently gave a keynote at www.pycon.co the first PyCon conference in Colombia. I spoke on Data Science Models in Production, lessons learned and the cultural aspects. I interviewed a Colombian Data Scientist – Juan Pablo Isaza Aristizábal 1. What project have you worked on do you wish you could go back to, and do better?… Continue reading Interview with a Data Scientist: Juan Pablo Isaza Aristizábal
I caught up with Greg Linden via email recently Greg was one of the first people to work on data science in Industry – he invented the item-to-item collaborative filtering algorithm at Amazon.com in the late 90s. I’ll quote his bio from Linkedin: “Much of my past work was in artificial intelligence, personalization, recommendations, search,… Continue reading Interview with a Data Scientist: Greg Linden
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