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

## Scraping?

The extraction of data from a website is known as *scraping*. A scraper makes a request to a web page for access either using the *HTTP* protocol or through a browser. The following steps are performed in receiving data from a web page.

* The scraper makes a *GET* request to a web page.

* In response, the structure of the web page, which contains all information in the _HTML DOM_ structure is returned.

* The scraper then parses the *HTML* structure to find the information that is required.

* The information is then fetched and stored.

> Any kind of data can be scraped from a web page, whether it be a text, video, audio, or image.


### How is it done?

The main part of scraping is to fetch the data from the *HTML DOM* structure after receiving the response from the web site. The request and response part can easily be done using built-in tools, but the efficient retrieving of data depends on the skill of the programmer.

Web scraping can be thought of like a spider crawling over each and every corner of an *HTML* structure and retrieving information when needed.

___
In the next lesson, the tool used for scraping is discussed.

# Scraping?

The extraction of data from a website is known as *scraping*. A scraper makes a request to a web page for access either using the *HTTP* protocol or through a browser. The following steps are performed in receiving data from a web page.

* The scraper makes a *GET* request to a web page.

* In response, the structure of the web page, which contains all information in the _HTML DOM_ structure is returned.

* The scraper then parses the *HTML* structure to find the information that is required.

* The information is then fetched and stored.

> Any kind of data can be scraped from a web page, whether it be a text, video, audio, or image.


## How is it done?

The main part of scraping is to fetch the data from the *HTML DOM* structure after receiving the response from the web site. The request and response part can easily be done using built-in tools, but the efficient retrieving of data depends on the skill of the programmer.

Web scraping can be thought of like a spider crawling over each and every corner of an *HTML* structure and retrieving information when needed.

___
In the next lesson, the tool used for scraping is discussed.

In this lesson, an introduction to scraping is provided.

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

What is Scraping?

Scraping?

How is it done?