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

The most common problem faced by any business is inventory management. Sometimes business owners either have too much of a product that is not being sold or too little of a product whose demand is very high. This can cause a substantial loss to a company's profits and reputation. For more information on this problem, refer [here](https://www.business2community.com/product-management/6-times-horrific-inventory-control-almost-killed-companies-01659644). 

If we somehow know what products from which brands and categories are selling the most in the market, then inventory management can be optimized to some level. Here, products from which brand and category were bought the most will be determined.

# Top brands

First, the data will be read and the `event_time` column will be converted to DateTime format. Then, the following steps will be performed to obtain the top brands.

In this lesson, the most famous brands and categories are fetched from our dataset.

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

Section 3: Identifying Famous Brands and Categories