Gain insights into data science fundamentals, explore machine learning and big data, and delve into real-time projects. Discover systematic approaches to data acquisition, wrangling, and solving diverse problems.

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There is a lot of dispersed, and somewhat conflicting information on the internet when it comes to data science, making it tough to know where to start. Don't worry. 

This course will get you familiar with the state of data science and the related fields such as machine learning and big data. You will be going through the fundamental concepts and libraries which are essential to solve any problem in this field. 

You will work on real-time projects from Kaggle while also honing your mathematical skills which will be used extensively in most problems you face. You will also be taken through a systematic approach to learning about data acquisition to data wrangling and everything in between. This is your all-in-one guide to becoming a confident data scientist.

An Introductory Guide to Data Science and Machine Learning


 
# Poisson Distribution

Poisson Distribution is a Discrete Probability Distribution. It is associated with the outcomes of experiments which are either success or failure. They occur during a time interval or in a specified region. These experiments are also called Poisson Experiments. Poisson Experiments are derived from the Poisson Process.

## Properties of Poisson Process 

 * The Poisson Process has no memory. This means that the number of outcomes occurring in a time interval or region are independent of the number of outcomes occurring in some other time interval.

* The probability that an outcome will occur in a small time interval or small region is proportional to the length of the time interval or the size of the region.

* The probability that more than one outcome will occur in such a short time interval or fall in such a small region is negligible.


## Probability Mass Function

The Probability Mass Function for a Poisson Distribution is 

$p(x) =\frac{e^{-\lambda t}(\lambda t)^x}{x!}, x = 0,1,2...$

* $e=2.71828 . . .$
* $x$ is the value associated with the outcome of the Random Variable X.
* $\lambda$ is the average number of outcomes per unit time, distance, area or volume.
* $t$ is the number of outcomes occurring in a given time interval or specified region.

## Example 

During a laboratory experiment, the average number of radioactive particles passing through a counter in 1 millisecond is four. What is the probability that six particles
enter the counter in a given millisecond?


## Solution

Here we can extract the following things out of the question

* $\lambda t=4$
* $x=6$

$p(6)=\frac{e^{-4}(4)^{6}}{6!}=\frac{75.0208}{720}=0.1042$

So, the probability that six particles enter the counter in a given millisecond is 0.1042. 




Learn about Poisson Distribution as knowledge of Probability Distributions is essential in the field of Data Science and it is the backbone for understanding many concepts.

What is Data Science ?

Applications of Data Science

Overview of Libraries

Probability and Statistics

Machine Learning Part-1

Machine Learning Part-2

Machine Learning Part-3

Deep Learning

Machine Learning Tools and Libraries

Big Data Tools and Technologies

Where to go next ?

Poisson Distribution

Poisson Distribution

Properties of Poisson Process