There are very few mailing lists that I remain subscribed to AND faithfully open on a regular basis. But Analytics Vidhya always has something interesting, something complicated explained simply, a step-by-step tutorial... Take, for example, the article I received this morning 10 matplotlib tricks - I so totally need this - it seems like a... Continue Reading →
Visualizing data overlaps
An example use case is this: you have a list of customers who have bought the various products that you sell. You want to know where the overlaps are, for example: How many customers who bought the Blue Widget also bought the Green Widget? Or what percentage of customers who bought the Blue Widget also... Continue Reading →
Data structures for deep learning
I recently completed the Udacity Deep Learning Nanodegree (highly worth doing by the way), which focuses on implementing a variety of deep learning architectures using PyTorch. At the outset, it's pretty fundamental to understand the data structures you'll be encountering as inputs to and outputs from your neural network architecture. What I noticed was that... Continue Reading →
How to – KMeans clustering
Clustering is a type of unsupervised learning. Us humans would think of it as 'categorization' perhaps. For example, if I gave you a bag of red, blue and white balls and asked you to sort them (without telling you how) you would probably naturally gravitate towards sorting them by colour as this would be the... Continue Reading →
How to – Principal Component Analysis
I always like to understand concepts well before I use them (which is good because it's the right thing to do, but bad because it slows me down a lot!), so it was with great excitement that I came across Matt Brems' article A One-Stop Shop for Principal Component Analysis recently. If you read this... Continue Reading →
Avoiding for loops in Pandas
There will be times when you are tempted to loop through rows or columns in Pandas to achieve your results - and the lesson I keep learning is Don't do it! Every time I'm tempted to write a for loop with Pandas data I find myself clock watching and cursing... 9 times out of 10 there... Continue Reading →
Data Science for Good Challenge – Kaggle
I picked Center for Policing Equity challenge on Kaggle for three reasons: I love maps and I love the idea that data scientists can significantly improve our world, in addition to improving the bottom lines of big corporates. And this is exactly the type of messy data one would get in the real world so... Continue Reading →
Pandas dataframe styling – cool!
I always like to visualize data and see the detail if possible so it was with great joy that I stumbled across DataFrame.style this morning. Here is an example of how it helps us to visualize some Titanic survival rates by sex and passenger class: The Pandas documentation itself is pretty comprehensive, but if you're looking... Continue Reading →
Regex can do amazing things with data cleanups - basically mandatory must use. But also tricky to retain in brain if not used frequently... Here are 3 great reference and test resources that can help: https://docs.python.org/3/howto/regex.html https://regexr.com/ https://regexone.com/
Getting results vs Understanding
Alexander Pope is famously quoted as saying: A little learning is a dangerous thing; drink deep, or taste not the Pierian spring: there shallow draughts intoxicate the brain, and drinking largely sobers us again. I've been thinking about these words the past few days as I worked on my latest challenge: a text classifier using... Continue Reading →
Sho't left to data science 