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

# Introduction
Wherever data needs to be displayed efficiently, and in a comprehensible manner, heatmaps are used. They visualize trends in data perfectly and enable us to analyze what to do next.

A heatmap uses different colors to display data, while other plotting methods either use height or width parameters for it. Specific colors in varying concentrations are applied to different values of data depending on their importance.

Let's visualize a heatmap. The data used in the following example can be obtained from this [link](http://bit.ly/2cLzoxH). The dataset contains information about the life expectancy of people from different countries and continents.

In this lesson, heatmaps using the seaborn package 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

Heatmaps

Introduction