If you are from a statistical background, it will be easier for you to understand and analyze your data. Every time we get the data, we need to explore it to understand the features and distributions of data. Understanding the theory of probability distributions that you need for data science will help you analyze your data. So, in this article, I will introduce you to the probability distributions for data science.
Probability Distributions for Data Science
Whenever we get a dataset, it doesn’t contain data for the entire population. It is simply a sample of the population that is nothing more than a subset of a field of study. In this sample, we perform statistical analysis to understand the patterns of the sample data so that we can make predictions about the entire population. Understanding probability distributions helps us understand the principles of analyzing different types of data distributions. Below are the most important types of probability distributions that you need to know for data science.
- Binomial Distribution
- Uniform Distribution
- Bernoulli Distribution
- Normal Distribution
- Poisson Distribution
- Exponential Distribution
So these are the most important types of probability distributions for Data Science that you should know. Now let’s go through all of them one by one for a brief understanding of these distributions.
Binomial Distribution:
The binomial distribution represents the number of times an event occurs in a fixed number of trials. For example, how often you get heads in 15 flips of a coin. Here are some of the conditions that satisfy the binomial distribution:
- In the binomial distribution, only two results are possible for each trial which should be mutually exclusive.
- Here, all the trials are independent so that the result of the first trial does not affect the next trial.
- The probability of an event occurring remains the same for each trial.
Uniform Distribution:
In a uniform distribution, all values fall between the minimum and maximum values with the same probability. Here are the conditions that satisfy a uniform distribution:
- The minimum and maximum values are fixed.
- All values fall between the minimum and maximum points with equal probability.
Bernoulli Distribution:
The Bernoulli distribution is a discrete distribution with only two possible outcomes. This is the reason why it is also known as the Yes / No distribution. In the binomial distribution, we have many trials, but in the Bernoulli distribution, we only have one trial that either succeeds or fails.
Normal Distribution:
The normal distribution is the most favoured type of data distribution in data science. It is the most preferred distribution in data science because it describes many complex models such as the IQ of people or the average height of people in a country. When the data is normally distributed, it means the mean is 0 and the standard deviation is 1.
Poisson Distribution:
The Poisson distribution represents the frequency of occurrence of an event in a particular interval. For example, the number of calls received in an hour or the number of errors found in a document. Here are some of the conditions that satisfy the Poisson distribution:
- The number of possible occurrences of an event in any interval is unlimited.
- The average number of occurrences must be the same from interval to interval.
Exponential Distribution:
The Exponential Distribution is widely used in the reoccurring events at random intervals of time. For example, the time lapse between the arrival of the next station. It generally means that time does not affect future outcomes.
Summary
So these were the probability distribution that you should know for data science. All the distributions are important, but among them, the normal distribution matters a lot because many times we need to convert the data into normal distribution while working on a data science task. I hope you liked this article on Probability Distributions for Data Science. Feel free to ask your valuable questions in the comments section below.
