Each year I supervise several data-science master’s students, and each year I find myself repeating the same advises. Situation has worsen since students started (mis)using GPT models. I therefore have written this blog post to highlight few important, and often overlooked, aspects of thesis-writing. Many points apply also to writing in general.
Tag: Personal opinions
From Excel to Script? Add Browsing Points
“If it ain’t broke, don’t fix it”.
On Writing Math
There are a lot of examples for skills that despite being greatly needed, we never get any formal training for. At least nothing is built into our core educational programs. Few examples are: how to read well, how to listen well, or how to develop your can-do mental attitude. Writing well, in particular math-writing, is another such example. Here I share few pointers from my own experience of reading and writing math.
R + Python = Rython
Enough! Enough with that pointless R versus Python debate. I find it almost as pointless as the Bayesian vs Frequentist “dispute”. I advocate here what I advocated there (“..don’t be a Bayesian, nor be a Frequenist, be opportunist“).
Nowadays even marginally tedious computation is being sent to faster, minimum-overhead languages like C++. So it’s mainly syntax administration we insist to insist on. What does it matter if we have this:
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xsquare <- function(x){ x^2 } |
Or that
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def xsquare(x): return x**2 |
Machine learning is simply statistics
Note: I usually write more technical posts, this is an opinion piece. And you know what they say: opinions are like feet, everybody’s got a couple.
Machine learning is simply statistics
A lot of buzz words nowadays. Data Science, business intelligence, machine learning, deep learning, statistical learning, predictive analytics, knowledge discovery, data mining, pattern recognition. Surely you can think of a few more. So many you can fill a chapter explaining and discussing the difference (e.g. Data science for dummies).
But really, we are all after the same thing. The thing being: extracting knowledge from data. Perhaps it is because we want to explain something, perhaps it is because we want to predict something; the reason is secondary. All those terms fall under one umbrella which is modern statistics, period. I freely admit that the jargon is different. What is now dubbed feature space in machine learning literature is simply independent, or explanatory variables in the statistical literature. What one calls softmax classifier in the deep learning context, another calls multinomial regression in a basic 1-0-1 statistics course. Feature engineering? Call it variable transformation rather.