Candlestick Mastery – Introduction

Candlestick mastery is understanding and using candlestick patterns to make informed trading decisions. Candlestick patterns are graphical representations of price movement, and they can be used to identify trends, reversals, and support and resistance levels. There are many different candlestick patterns, but some of the most common and important ones include: Bullish patterns: Bullish patterns suggest …

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Essentials of Data Science – Probability and Statistical Inference – Normal Approximation

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 and continuous 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, Geometric distribution, and Normal …

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Python Fundamentals – String

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 …

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Python Fundamentals – Dictionary

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 …

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Essentials of Data Science – Probability and Statistical Inference – Normal Distribution

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 …

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Essentials of Data Science – Probability and Statistical Inference – Continuous Uniform Distribution

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 …

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Essentials of Data Science – Probability and Statistical Inference – Geometric Distribution

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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Essentials of Data Science – Probability and Statistical Inference – Poisson Distribution

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, …

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Essentials of Data Science – Probability and Statistical Inference – Bernoulli and Binomial Distributions

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 …

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