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

## Transpose
As the name suggests, we will take the transpose of a given 2-D `NumPy` array. Just like matrix transpose in linear algebra, we convert the rows of a 2-D NumPy array into columns and convert its columns into rows. The function `transpose()` or simply `T` can be used to take the transpose of a 2-D array.

# Transpose
As the name suggests, we will take the transpose of a given 2-D `NumPy` array. Just like matrix transpose in linear algebra, we convert the rows of a 2-D NumPy array into columns and convert its columns into rows. The function `transpose()` or simply `T` can be used to take the transpose of a 2-D array.

In this lesson, transposing of NumPy arrays are explained.

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

Transposing of NumPy Array

Transpose