I’ve been working on software products for several years now. And while a lot of my work has been greenfield, I’ve struggled to articulate the different kinds of work that happens in product development. Des Traynor one of the founders of Intercom had a great talk about this. (Source: Video) Often people don’t understand the two… Continue reading The two kinds of work in Software Products
I recently caught up with Noelle Saldana and she shared some of her experiences as a Data Scientist and Product Management expert. She’s got a strong technical background and has worked for Pivotal a leading technology consultancy helping Forbes 500 learn how to leverage Machine Learning. She’s recently joined Salesforce where she’ll be wearing a… Continue reading “Much of our day-to-day lives are touched and improved by Data”
I’ve got an online course called Probabilistic Programming Primer You may be wondering what that’s all about, so I put together a video explainer all about it. Building easy to interpret models isn’t a nice to have anymore it is the reason people pay for models in the first place. And as we see more… Continue reading Introduction to Probabilistic Programming Primer
I recently put together a survey of over 100 data scientists and analysts. There’ll be a report coming super soon, but before then I wanted to share the infographic. We’ll go into more detail in the report. If you’d like to get access to the report you should sign up here 70 percent of data scientists… Continue reading State of PPL: How are Bayesian methods used in industry?
I’ve been contributing on and off to PyMC3 and other projects for a few years now. I’m still learning a lot about Bayesian Statistics and building software. I intend to continue to work on this stuff. Chris Fonnesbeck – recently did a talk at NeurIPS where he talks about some of the stuff we’ve learned in… Continue reading 3 key lessons from being an OSS developer
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 on https://www.datacamp.com/community/podcast/human-centered-design-data-science at 38:10 talking about interpretability and fairness in Machine Learning.
Inspired by Vicki I decided to build a Tweetbot – the code is available here. You can follow the tweetbot online – the architecture that Vicki proposed is basically what I did, only I made a few changes in the code. Time taken It’s worth pointing out first that it took me approximately 32 hours of… Continue reading How to use AWS Lambda to build a tweetbot
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