A string is a sequence of characters. Generally, computers do not deal with characters, they deal with numbers. Even though you may see characters on your screen, internally it is stored and manipulated as a combination of 0’s and 1’s. The conversion of a character to a number is called encoding, and the reverse process …
Python Fundamentals - String Read More »
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Python dictionary is an unordered collection of items, which means we can’t index it. In comparison, other compound data types have only value as an element.A dictionary has a key: value pair, and it can be visualized as a table containing two columns, a key column, and a value column to get the value given …
Python Fundamentals - Dictionary Read More »
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In this note series on Probability and Statistical Inference, we have already seen the importance of probability distributions and their associated probability functions for discrete random variables. In addition, we have learned to resemble a natural random phenomenon with these probability distributions. These distributions were Degenerate distribution, Uniform distribution, Bernoulli distribution, Binomial distribution, Poisson distribution, and Geometric distribution. This note will cover probability distributions and …
Essentials of Data Science – Probability and Statistical Inference - Normal Distribution Read More »
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In this note series on Probability and Statistical Inference, we have already seen the importance of probability distributions and their associated probability functions for discrete random variables. These distributions were Degenerate distribution, Uniform distribution, Bernoulli distribution, Binomial distribution, Poisson distribution, and Geometric distribution. In addition, we have learned to resemble a natural random phenomenon with these probability distributions. This note will cover probability distributions and …
12,184 total views
In this note series on Probability and Statistical Inference, we have seen the importance of probability distributions such as Bernoulli distribution, Binomial distribution, and Poisson distribution, and how these distributions resemble a real random phenomenon. This note will cover the basic intuition behind Geometric distribution, expectation, variance, and other quantitative measures to characterize the geometric random …
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In the previous note on Probability and Statistical Inference, we have seen the importance of probability distributions. This note will cover the basic intuition behind Poisson distribution, expectation, variance, and other quantitative measures to characterize the random variable. Further, we will also cover various similar phenomena following Poisson distribution. Using the probability functions of Poisson distribution, …
12,106 total views
In the previous note on Probability and Statistical Inference, we have seen the importance of probability distributions. This note will cover the basic intuition behind Bernoulli and Binomial distributions, expectation, variance, and other quantitative measures to characterize the probability distribution. Further, we will also cover various similar phenomena following Bernoulli and Binomial distributions. Using the probability …
22,198 total views
In the previous note on the Probability and Statistical Inference, we have seen the the important concept of probability and statistics which are as follows: At the very first, we have seen the basic theory of probability and how to model a random phenomenon by satisfying the axioms of probability. Further, we explore the random variables …
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In the previous note on the Probability and Statistical Inference, we have learned expectations, moments, skewness, and kurtosis to measure the central tendency, dispersion, symmetry, and peakedness of probability curve or distribution, respectively. Introduction We define quantiles in terms of the distribution function. The value for which the cumulative distribution function is: is called the p-quantile. Here, is a value …
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In the previous note on the Probability and Statistical Inference, we have learned expectations and moments for the probability distribution of a random variable which gives central tendency and variability of the values of a random variable, respectively. In this note, we will further extend the concept of moments and study the other characteristics precisely the shape and peakedness, of the probability …
104 total views, 1 views today
