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

# Stock market

A stock market represents the claims of companies and individuals on the shares of public companies. The prices of these shares vary depending on various factors, including inflation, demand, and reputation.

Buying and selling stocks is a major business, and people keep trying to predict the future stock behavior to earn large paybacks on their investments. 

In this data analysis project, we try to analyze the data of various stocks to obtain valuable information. This information provides insights on which stocks generate more returns.

> **Note:** There is no program in the world that can correctly predict future stock behavior. The techniques covered in this project only allow us to make an educated guess based on the previous behavior of a particular stock.

## Some stock market jargon

* Closing price: The price of the stock when the market closes that day

* Daily returns: The increase or decrease in the percentage amount from the previous day

* Risk: The amount that can be lost

* Stock behavior: Whether the stock price would go up or down

# Let's dive in

The stock data of the following four technology companies in Pakistan will be analyzed.

* **Systems Ltd**
* **NETSOL**
* **PTCL** 
* **Avanceon**

First and foremost, we need the historical data of these companies. The data is available [here](https://dps.psx.com.pk/historical) and can be viewed by placing the relevant time period and company names in the search fields. A scraper will be used to pull all the data and put it into _CSV_ files. 

The code for the scrapper is available below, and the *chromedriver* software can be downloaded from [here](https://chromedriver.storage.googleapis.com/index.html?path=80.0.3987.16/).

In this lesson, you will learn about the stock market and how you can analyze its data to extract useful insights from it.

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

Stock Market Analysis

Stock market