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The article is organized into two parts:
I. Math Refresher
II. Questions from data science interviews related to the topic
Essential math for Data Scientists explained from scratch, writes Agnieszka Kujawska, PhD, published in Towards Data Science.
Moments are set of statistical parameters used to describe a distribution. The calculations are simple, so are often used as a first quantitative insight into the data. A good understanding of data should always be the step before training any advanced ML model. It allows minimizing the time required to choose the methodology and interpret results.
In physics, moments refer to mass and inform us how the physical quantity is located or arranged. In math, moments
refer to something similar — the probability distribution — a function
that explains how probable are different possible outcomes of an
experiment. To be able to compare different data sets we can describe
them using the first four statistical moments:
1. The expected value
2. Variance
3. Skewness
4. Kurtosis
Let’s go through the details together!...
Remember that the most efficient way to learn (math) skills is by practice. So don’t wait until you feel ‘ready’, just grab a pen and paper or your favourite software and try few examples on your own. I keep my fingers crossed for you.
Source: Medium
