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

## Stocks data
The stock data for **Systems Ltd**, **NETSOL**, **PTCL**, and **Avanceon** for the year *2018* will be used for this analysis. Any stock data has six important columns.

* **Time**: The date and time of the day
* **Open**: The price of the stock at the start of the day when the market opens

* **High**: The highest price of the stock on that day

* **Low**: The lowest price of the stock on that day

* **Close**: The price of the stock at the end of the day when the market closes

* **Volume**: The number of stocks traded that day

Let's look up the stocks data for **Systems Ltd**.

# Stocks data
The stock data for **Systems Ltd**, **NETSOL**, **PTCL**, and **Avanceon** for the year *2018* will be used for this analysis. Any stock data has six important columns.

* **Time**: The date and time of the day
* **Open**: The price of the stock at the start of the day when the market opens

* **High**: The highest price of the stock on that day

* **Low**: The lowest price of the stock on that day

* **Close**: The price of the stock at the end of the day when the market closes

* **Volume**: The number of stocks traded that day

Let's look up the stocks data for **Systems Ltd**.

In this lesson, stock price trends and the concept of moving averages are discussed.

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 1: Price Trends

Stocks data