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

In this challenge, different datasets made available by `seaborn` are used to plot different visualizations.

## Heatmap
The `flights` dataset will be used to plot a heatmap of its values. The contents of the data can be viewed [here](https://github.com/mwaskom/seaborn-data/blob/master/flights.csv). The task is to reshape the `DataFrame` using the `pivot` function and plot it on a heatmap just like explained [here](https://www.educative.io/courses/predictive-data-analysis-with-python/heatmaps). The `year` column of the dataset will be set as columns, the `month` will be set as row indexes, and the `passengers` will be the values. After reshaping, use the `heatmap` function of seaborn to plot the visualization.

In this challenge, different datasets made available by `seaborn` are used to plot different visualizations.

# Heatmap
The `flights` dataset will be used to plot a heatmap of its values. The contents of the data can be viewed [here](https://github.com/mwaskom/seaborn-data/blob/master/flights.csv). The task is to reshape the `DataFrame` using the `pivot` function and plot it on a heatmap just like explained [here](https://www.educative.io/courses/predictive-data-analysis-with-python/heatmaps). The `year` column of the dataset will be set as columns, the `month` will be set as row indexes, and the `passengers` will be the values. After reshaping, use the `heatmap` function of seaborn to plot the visualization.

This lesson provides a few exercises to test your understanding of the seaborn module for visualization.

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

Challenge: Visualizing Seaborn Datasets

Heatmap