Probabilistic Programming versus Machine Learning In the past ten years, we’ve seen an explosion in Machine Learning applications, these applications have been particularly successful in search, e-commerce, advertising, social media and other verticals. These applications have been particularly focused on predictive accuracy and often involve large amounts of data — sometimes in the region of terabytes — in fact this… Continue reading Why would I ever NEED Bayesian Statistics?
I’m seeing a very fuzzy line between where technical business analysts end & IT teams begin. Y’all know of good articles/books on managing responsibilities when coding analysts on the biz side interface with IT dev teams? Seems tricky. #rstats #pydata #python — JD Long (@CMastication) October 19, 2018 My friend JD Long, has been a… Continue reading I’m an Analyst and the software engineers made fun of my code!
I’ve been thinking and discussing with various people lately – ‘career path for data science’. Someone said to me recently: Go become a research scientist and specialise in specific machine learning models say NLP at a specific company such as Google, Amazon, etc. Become a data scientist at a startup or growth company and accept… Continue reading Adding value as Data Scientists
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
I’ve been programming professionally for about 5 years now. So I don’t consider myself a brilliant expert. Never the less one thing I think that I’ve learned is that it isn’t about the ‘technical problems’ it’s about whether or not you solve a real-world or meaningful problem. Some people call these ‘business problems’ but they… Continue reading Why Zalando’s tech radar sucks as a stack
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 was recently chatting to a friend who works as a Data Science consultant in the London Area – and a topic dear to my heart came up. How to successfully do ‘AI’ (or Data Science) in the enterprise. Now I work for an Enterprise SaaS company in the recruitment space, so I’ve got a certain… Continue reading AI in the Enterprise (the problem)