One of my favourite data science resources is the mini-episode series of the Data Skeptic podcast. These short episodes would feature the host explaining a data science concept to a non-expert in plain English.
I wanted to share a few of these with some colleagues from work and thought I’d catalogue them here.
I was recently interviewed on the Talk Python podcast about my background in analytics and how I’m applying it in the energy sector: Python in the Electrical Energy Sector
AEMO’s Quarterly Energy Dynamics (QED) report just came out today for Q1 2021. Of particular interest is the South Australian region which has been experiencing incredibly low prices thanks to the high levels of renewables in the market.
At the same time, these low prices have created system security challenges for the market operator. In fact over the past quarter, AEMO has had to intervene in the market a record 70% of the time to ensure that at least one gas powered generator remained running.
I’ve had a few recommendations lately from experienced programmers that I should learn a “pure” functional language. This is supposed to allow me to think about problems and programming in a different way. I’ve used some functional aspects of Python before (like map), however, this will be my first real attempt at a proper functional language: Haskell.
As I’ve been reading up on the language and running through tutorials, I thought I’d start up a list of learning materials I found rather helpful:
Lately, myself and a friend have become rather interested in learning more about machine learning. I’ve been trying to start a collection of learning materials that I either found useful or mean to go through at some point, and thought I’d write a post about it. I’m hoping this can help people who are just starting out and those who know more than me could comment and point me towards some additional material I can link to and look into myself.
**Update** Had a great response on Reddit, thanks so much for all the suggestions that have flooded in! I’ve added quite a few new links that looked good, and am currently reviewing some other materials I’ll link to soon. This list will continue to evolve with links to new content as I find them (or am alerted by others). Follow me on Twitter for updates and new posts on machine learning, research and statistics.
Teaching Materials
Talks
iPython Notebooks
R Markdown
MOOCS
Interesting Links
OpenCV Machine Learning
Books
Podcasts
Amazon Web Services (AWS)
I’ve been following some of the great data school tutorials and I recently read a couple of interesting posts about teaching programming and data science on their website:
The second article is a little longer, but well worth the read. When I first started trying to learn about machine learning, I thought R would be the natural choice. However, I’ve become a real fan of Python’s Scikit-Learn (Six reasons why I recommend scikit-learn) lately and have continued my learning in Python. I can definitely see why they made that decision with their teaching, and the ability to use the Anaconda Python installation to get up and running definitely makes a lot of sense.
Getting back to the original purpose of this post, I liked the teaching idea he discusses in the article of starting the class off with a motivating example – in their case they explored the iris dataset and used the K-nearest neighbours algorithm. I also thought it was a great idea to use data retrieval as a lesson opportunity rather than merely downloading a bunch of CSV files from a website.
Finally, one thing I think would have been handy would be for them to give a tour of Unix/Linux and Bash as part of the course materials, but then I guess you have to draw the content line somewhere when time is limited.
Programming
Organisation
Bioinformatics
Lately I’ve become increasingly interested in using Git for version control of my programs. Git is a really good way to track changes made to your code either when you’re coding by yourself, or in a company with dozens of other developers. I’ve only really had a relatively superficial understanding of how Git worked before, and I tended to find a lot of the explanations online rather confusing. However, if you want to learn how to use Git, then I can thoroughly recommend this website, which will teach you how to use Github with an interactive terminal in the browser.
Once you get through those, then I would watch these two videos and then bookmark them for future reference.
**Update** I’ve found a few other resources for learning more about Git:
I’ve been subscribed to Jeff Knupp’s Python programming blog for a while now, and have really enjoyed a lot of the content. He recently released a 3-part video series on writing idiomatic Python, which I highly recommend watching.
C++ is the most recent language I’ve acquired, and while I certainly still have a lot to learn, I’ve gained a reasonable understanding from the following free resources online:
**UPDATE**
I was recently alerted to the fact that some of these resources can contain incorrect information/bad practices. Here’s a great list of C++ resources which have been vetted by the reddit learnprogramming community. One recommended resource is a book titled “C++ Primer 5th edition”. It was published in 2012 so it covers the C++11 standard.