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

# Histogram
This visualization is used to display the distribution of numerical data as accurately as possible. This plot divides the data into a specified number of bins, thus separating the data into a range of values. Then, it sketches bars to show the number of data points that each bin contains.

## Using matplotlib
*Matplotlib* provides the `hist()` function to plot a *histogram*. The following example implements a histogram of _two-hundred_ random values.

import numpy as np
import matplotlib.pyplot as plt

set1 = np.random.randn(200) # Generating random data

plt.hist(set1, edgecolor='black', color='red', bins=20) # plotting histogram

Two additional parameters are passed to the `hist()` function. The `color` parameter defines the color of the plot. The `edgecolor` parameter defines an outline around each bin. The `bins` parameter defines how many sections the data is distributed into.  By default, the number of bins is set to **ten**.
The output can be zoomed in, revealing that the random _two-hundred_ values are divided into exactly **twenty** sections. 
### Multiple histograms
*Matplotlib* provides functionality to visualize one histogram on top of another to compare different sets of data. The overlapping points of the two datasets can be clearly displayed using this technique.

In this lesson, histogram plots with matplotlib and seaborn 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

Histogram Plots

Histogram

Using matplotlib