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

## Grouping

Grouping arranges similar data of a `DataFrame` in groups. If a value occurs in multiple rows of a single column, the data related to that value in other columns can be grouped together. This method also enables us to perform various *aggregation* operations on the grouped data like *sum*, *avg*, *mean*, etc.



# Grouping

Grouping arranges similar data of a `DataFrame` in groups. If a value occurs in multiple rows of a single column, the data related to that value in other columns can be grouped together. This method also enables us to perform various *aggregation* operations on the grouped data like *sum*, *avg*, *mean*, etc.



In this lesson, an explanation is provided on data grouping and its various techniques.

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

Data Grouping

Grouping