You will never be a data scientist without knowing #Calculus, #Probability and #InformationTheory.
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Solving an interesting probability problem using Python and tensorflow-probability, my new article in Towards Data Science (Online Publication)
https://lnkd.in/gH9pkUG
#probability #python #datascience #technology
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https://lnkd.in/gH9pkUG
#probability #python #datascience #technology
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#Statistics has many uses but, fundamentally, it's a systematic way of dealing uncertainty. When something is certain, there is no need to bring in a statistician or ask anyone for their council.
Since we're concerned with uncertainty, statisticians approach questions probabilistically. To conclude that something is likely to be true does not mean we're claiming it IS true, only that it's more likely to be true than not.
We may estimate this probability as being very high but, again, this is not saying the #probability is perfect (1.0).
Statisticians also think in terms of conditional probabilities, which means we've estimated the probability after having taken other information into account.
For instance, we might estimate the probability of a person buying a certain type of product within the next three months as 0.7 because he is a 25 year-old male. This estimate may have been made with a statistical model and data from thousands or millions of other consumers. For a 55 year-old woman our estimate might be 0.15.
Part of the challenge of being a statistician is that decision-makers often come to us for definitive yes-or-no answers. They can become irritated when we ask for more information or give them very qualified recommendations.
It ain't just math and programming!
tips: If someone says, for example, that A is not the only possible explanation for something and that B, C, or D are other possibilities, a common reaction is for the other party to conclude the first person is saying A is NOT a possible explanation. Humans are funny people.
✴️ @AI_Python_EN
Since we're concerned with uncertainty, statisticians approach questions probabilistically. To conclude that something is likely to be true does not mean we're claiming it IS true, only that it's more likely to be true than not.
We may estimate this probability as being very high but, again, this is not saying the #probability is perfect (1.0).
Statisticians also think in terms of conditional probabilities, which means we've estimated the probability after having taken other information into account.
For instance, we might estimate the probability of a person buying a certain type of product within the next three months as 0.7 because he is a 25 year-old male. This estimate may have been made with a statistical model and data from thousands or millions of other consumers. For a 55 year-old woman our estimate might be 0.15.
Part of the challenge of being a statistician is that decision-makers often come to us for definitive yes-or-no answers. They can become irritated when we ask for more information or give them very qualified recommendations.
It ain't just math and programming!
tips: If someone says, for example, that A is not the only possible explanation for something and that B, C, or D are other possibilities, a common reaction is for the other party to conclude the first person is saying A is NOT a possible explanation. Humans are funny people.
✴️ @AI_Python_EN