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

# Scatter plot
A scatter plot represents the values of data as points in a _cartesian plane_. It displays the data points based on the cartesian coordinates on an XY-plane.
It is preferably used when a pair of dependent and independent variables need to be represented or visualized.

The same example from the [regression](https://www.educative.io/courses/predictive-data-analysis-with-python/regression-plots) plot lesson will be used to display a scatter plot.

import numpy as np
import seaborn as sns

df = sns.load_dataset('tips')

sns1 = sns.scatterplot(x = 'total_bill', y = 'tip', data = df)

Just like in the *regression* lesson, the `total_bill` is an independent variable, and `tip` is the dependent variable. The `total_bill` is on the x-axis, and the `tip` is along the y-axis. A circle is placed on the graph where the values of these two planes coincide.

For more functionalities and information on scatter plots, refer [here](https://seaborn.pydata.org/generated/seaborn.scatterplot.html).

# KDE (kernel density estimation) plots

This function calculates and plots the probability density of a given dataset, which means that it estimates the value of a random variable. Let's understand this concept from an example.

In this lesson, scatter plots and KDE plots 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

Scatter and KDE Plots

Scatter plot