In the fast moving world like this, it is evident that we have entered the globe of big data. There is a lot of information available today, and we need to process them mathematically with the assistance of statistics for meaning outcomes. We come across data in different forms as a part of our day to day activities. New technology or software like Hadoop has solved the main issue of storing an enormous amount of data. While a data scientist will have the knowledge of statistics than a software engineer and hence a data scientist is considered as the future phase of artificial intelligence by the experts.

**What is the probability distribution?**

Probability distribution gives the probability of an event that is likely to occur in a given set of circumstances. A probability distribution can be explained with formulas or plotted through graphs for easy interpretation of the data. It is the most common way of describing the probability of an event. A probability distribution function may be any function used to define a specific probability distribution. A probability distribution table is a result of equations that connects every outcome of an event with its probability of occurrence. A mean of the probability distribution is depicted by the average value of the variables in the particular distribution. Mean, median and mode are the vital part of the probability distribution.

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**Types of data**

Before digging deep into the different types of probability distribution let us know about the types of variables used in these distributions. Data can be either discrete or continuous in nature. Discrete variables are those that have an outcome out of a specific set of variables. A simple example is a six-faced die when you roll the die the possible outcomes are 1, 2, 3, 4, 5 or 6.

Whereas continuous data may take up any value out of the given range. Here the given range may either be finite or infinite. Example of continuous data is the height of a girl which may be 4.5 feet.

**Types of the probability distributions**

Heading towards one of the easiest probability distribution that is Bernoulli distribution.

**Bernoulli distribution **

Here the outcome has only two possible ways. The two possible outcomes are success or failure and are denoted by 1 or 0 respectively. Which essentially means to say that a random variable X may be a success if takes the value 1 or failure if it takes the value 0. Here the probability of success and failure may not be the same.

**Uniform distribution**

To understand uniform distribution better let us get back to the rolling of a die example wherein the possible outcomes are equally likely to appear than the other. This type of probability distribution is deemed to be a uniform distribution.

**Binomial distribution**

A binomial distribution is a type of probability distribution where only two possible outcomes are probably success or failure, win or lose and more. Here the probability of both the outcomes is the same for all the trials.

**Normal distribution**

A normal distribution is symmetric above the mean which means that the data near the mean is more likely to occur as opposed to the data that is far from the mean.

**Poisson distribution**

For events occurring at random point of time and the matter of interest is the number of times an event has occurred Poisson distribution is used.

**Exponential distribution**

The exponential distribution is highly used for survival analysis purposes. An example of exponential distribution is the lifespan of a machine.

**Conclusion**

As we know data science is a vast subject of analyzing data, statistics is an important tool or an essential component used by data scientists for arriving at a conclusion. The occurrence of the probability distribution is evident in many events of life, and hence it becomes a mandate to understand types of probability distribution for a data scientist.

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