Sho't left to data science

Tag: how to

  • A goldmine of information…

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    shotlefttodatascience
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    I’ve just discovered the awesome Brandon Rohrer and his blog while trying to find an intelligible article on Bayesian inference. What a goldmine – this guy is a born educator! Thank you for sharing your knowledge – it is well-appreciated!

  • SQL CheatSheet

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    shotlefttodatascience
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    I’ve just worked through Imtiaz Ahmad’s Master SQL for Data Science on Udemy and it was a thoroughly enjoyable, morale-boosting experience! He build on each concept so you never feel left behind or perplexed at how he arrived at a solution, and as promised there are a gazillion exercises so by the time you’re done you feel like…

  • Poisson vs Exponential distributions

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    shotlefttodatascience
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    These distributions are related yet different – here’s a comparison that hopefully clears up any confusions! Poisson Exponential Number of events that occur in an interval of time Time taken between 2 events occurring For example… the number of Metrorail trains that arrive at the platform in an hour For example… the time between one…

  • A Scrum fan is born… cheatsheet

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    shotlefttodatascience
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    I’ve just finished listening to The Art of Doing Twice the Work in Half the Time on Audible and I feel like a real fan already – I can’t wait to test-drive it in a team situation! As a stalwart of Corporate IT, I’ve only ever worked according to the “waterfall” methodology and I’m unpleasantly…

  • Law of total probability – worked examples

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    shotlefttodatascience
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    According to Wikipedia the law of total probability “expresses the total probability of an outcome which can be realized via several distinct events”. We can also think of this as the marginal probability: irrespsective of what road we took to get to this outcome, what is the total likelihood of the outcome occurring? Example 1…

  • Expected value refresher

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    shotlefttodatascience
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    The expected value of an event is its most likely outcome. Assign each potential result a probability. The expected value is sum of all the potential results x their respective probabilities: ∑ (potential_result1 x probability1,… potential_resultn x probabilityn) Consider the simplest example possible, the coin flip. You’ll be paid R10 if you pick tails, but…

  • Bash: command not found

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    shotlefttodatascience
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    This morning I’ve been playing “spot the difference” – a simple enough task: use a bash shell script to get 2 strings from a user (first name and second name) and then evaluate whether the 2 strings are the same or different. I had a template for the script and everything from the edX course,…

  • Getting results vs Understanding

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    shotlefttodatascience
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    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…

  • Small simple datasets for practising

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    shotlefttodatascience
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    It’s all very well downloading complex datasets from Kaggle and similar sources to play with – they’re amazing for learners because the data is always less clean than you would have hoped, more complex than you anticipated, and every bit as interesting as promised. BUT if you’re learning a new concept it’s easier to have…

  • Adding labels to districts in GeoPandas

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    shotlefttodatascience
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    Once you have your districts drawn up nicely, using the polygons from your shapefile, it would be useful to be able to label them – but of course you need to be able to tell GeoPandas where to place these labels via co-ordinates or points – and in your shapefile you only have polygons which…