Gain insights into performing predictive data analysis using Python. Learn about tools like NumPy, Pandas, Matplotlib, and Seaborn, and applying techniques to real-world financial and advertisement projects.

seaborn.tar.gz

seaborn

In this course, you will learn how to perform predictive data analysis using Python. The ideal audience is those who want to start their careers as data analysts. The main goal of this course is to show you how to use statistics to draw useful insights from data which can help in predicting future behavior or patterns.

Beyond that, you’ll learn all the tools of the trade that data scientists use everyday including: NumPy, Pandas, Matplotlib, and Seaborn. You’ll learn not only how to extract meaningful insights from data, but you’ll also learn how to create stunning visualizations that you can use for reports.

Various datasets of real-world scenarios are used in each lesson to get you accustomed to handling any type of data. At the end of the course, you will work on two real-world projects that demonstrate how data analysis techniques are being used in the financial and advertisement sector to generate revenue.

Predictive Data Analysis with Python

# Definition
The probability distribution function informs us what the probabilities of a range of outcomes defined in a random variable will be. 

$$R_{x}=[1,2,3,4]$$
$$P_{x}=[0.95, 0.02, 0.01, 0.02]$$

Here, $R_{x}$ is the random variable containing some outcomes, and the values in $P_{x}$ represent the probabilities of those outcomes, respectively.

Probabilities can be displayed of simple occurrences like tossing a coin to more complex occurrences, like the probability of a specific steroid treating a disease or not.

## Data types
There are mainly two types of data that we encounter while figuring out the probability of events:

* **Discrete data:** A random variable **X** has discrete data if the data it contains is finite, countable, and has specific values. For example, the outcome of a number of students in a class can only be a specific number like **50** or **60** and cannot be **50.5** or **57.7**. If a die is rolled, the only outcomes are **1, 2, 3, 4, 5, and 6**, which are finite, countable, and specific whole numbers.

* **Continuous data:**  A random variable **X** has continuous data if all the data points it contains are in specific ranges. The range can either be finite or infinite. For example, if we have the data for heights of different buildings, we cannot find the probability that the height of a building is exactly **700** cm, but we can find the probability that the height of a building is between **650 - 750** cm. The height can be **700.075** or **699.874**, but not exactly **700**. 


## Types of the probability distribution

There are different types of probability distribution functions used to model various kinds of data. For this course, we will discuss the most commonly used probability distributions.

### Uniform distribution

Events, where each and every outcome in a random variable has the same probability of occurrence will create a __uniform distribution__ of probabilities. This means that if a random variable has __n__ number of outcomes, each of those outcomes has a probability of occurrence equal to __1/n__. 

Uniform probability distribution can be created for both discrete and continuous data.

* __Discrete uniform distribution__: It uses discrete data and contains a finite number of outcomes that all have the same probability of occurrence. For example, a rolling die and a coin toss are excellent examples of this. The probability of all the outcomes of both of the above cases is the same, and since they contain a finite number of outcomes, they form a discrete uniform distribution.

* __Continuous uniform distribution__: It uses continuous data, and the outcomes are either in a range or infinite and have the same probability of occurrence. For example, a random number generator is an excellent example of this. In this example, there is an endless number of outcomes that can exist, and every number has an equal possibility of occurrence. So, a continuous uniform distribution is formed for this.



In this lesson, probability distribution and its types are discussed.

Getting Started

Numpy for Python

Pandas for Python

Statistics for Data Analysis

Data Wrangling

Visualizing the Data

Data Scraping

Project #1

Project #2

Conclusion

Predictive Data Analysis Exam

Probability Distributions

Definition