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

The website namebio.com will be scraped in this lesson. This site contains the domain name information like which domain is sold when, for what price, and where it is parked. In free mode, we can look at _one-hundred_ data points with four columns of data which include the **domain name, domain price, date it is sold, and the site where the domain is parked**. The data is displayed and available in the form of a table like in the following image.





In this lesson, you will build a proper automated scraper to store domain name data in a csv file.

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

Scraping Domains