For this episode, I speak with Hugo Bowne-Anderson; a data scientist at DataCamp (an educational platform for learning to code) and host of the DataFramed podcast.
The idea for asking Hugo to appear on this episode, was to chat about learning a programming language. Because for some traders, having the ability to write code can have great advantages—such as having the ability to collect stats on market behavior, perform research in a robust data-driven way, visualize large amounts of data, backtest and analyse trading ideas, implement algorithmic strategies, etc.
Plus more professional trading firms and finance related positions now require applicants to have some programming skills. And the same goes for many industries, which should be no surprise, considering a recent IBM study revealed that ‘90% of the world’s data has been created in the last two years alone.’
Hugo and I discuss when someone should consider learning to code, determining what’s relevant, the time it takes to become fluent in a programming language, working with new datasets, what to be wary of when using predictive models. And for fun, I ask Hugo (as a data scientist) how he’d go about creating a basic strategy…
Links and resources mentioned:
- DataFramed [Podcast]
- @HugoBowne [Twitter]
- Yves Hilpisch [Episode 84]
- A Gallery of Interesting Jupyter Notebooks [GitHub]
- Open Data Science Conference
- Fast Forward Labs
- Two Sigma [Kaggle]
- Wes McKinney [QZ]
- @BigDataBorat [Twitter]
- Weapons of Math Destruction [Amazon]