**Are You Struggling with Your Probability Assignment?**

Probability, also known as chance, is the likelihood of an event occurring. It is usually quantified as a number between 0 and 1. In this case, 1 indicates uncertainty while 0 indicates impossibility. Our daily life is not new to uncertainty and randomness. As a result, we need a good knowledge of probability to help us make informed judgments in cases of uncertainties.

Data Science is a field that uses statistical inferences to analyze and make predictions regarding data trends. Statistical inferences on the other hand use probability of distribution of data. Therefore, gaining extensive knowledge of probability and its applications can help you handle Data Science problems effectively.

**Conditional Probability**

This is the measure of the chance of a particular situation occurring given that another event has occurred. A number of Data Science techniques use the Bayes’ theorem to update the probabilities of hypothesis while considering a given evidence.

**Random Variable**

These are sets of possible outcomes from a random phenomenon. A random variable is also known as a stochastic variable or aleatory variable. It can be continuous or discrete. Continuous random variables can take any value while discrete random variables can only take specific values.

**Probability Distributions**

In the case of a random variable, probability distribution is used to describe the distribution of probabilities over the values of the random variable. There are two main types of probability distribution. Binomial distribution which has n (n= number of trials) repeated trials and each trial has only two possible outcomes (success or failure), and Normal distribution which is also called Gaussian distribution and is symmetric about the mean. This mean that data around the mean occur more frequently than those far away from the mean.

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