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

## Daily returns

The price of stock changes on a daily basis. Daily return calculations tell us how much the current day stock value is different from the previous day stock value. A positive change indicates a rise in the value of the stock while a negative change indicates a fall in the value of the stock. A stock with minimal positive or negative change is considered to be a stable and good stock.

As seen in the previous lesson, only **Systems Ltd** and **Avanceon** stocks provide promising results for the year *2018*. So, daily returns of **Systems Ltd** will be calculated and visualized in this lesson as this company had a higher value compared to **Avanceon**. Results for any company can be obtained by simply changing the name for the company file as discussed before. 

### Calculating daily returns

We don’t need to figure out the formula or steps for calculating the daily returns because `pandas` already provides a built-in function for it. The `pct_change` function is called from a `Series` object, and it calculates the daily return for all rows based on the current and previous row value. This function returns a new `Series` with the calculated daily returns. The first row has no, or `NaN`, value as there is no previous value for it.




# Daily returns

The price of stock changes on a daily basis. Daily return calculations tell us how much the current day stock value is different from the previous day stock value. A positive change indicates a rise in the value of the stock while a negative change indicates a fall in the value of the stock. A stock with minimal positive or negative change is considered to be a stable and good stock.

As seen in the previous lesson, only **Systems Ltd** and **Avanceon** stocks provide promising results for the year *2018*. So, daily returns of **Systems Ltd** will be calculated and visualized in this lesson as this company had a higher value compared to **Avanceon**. Results for any company can be obtained by simply changing the name for the company file as discussed before. 

## Calculating daily returns

We don’t need to figure out the formula or steps for calculating the daily returns because `pandas` already provides a built-in function for it. The `pct_change` function is called from a `Series` object, and it calculates the daily return for all rows based on the current and previous row value. This function returns a new `Series` with the calculated daily returns. The first row has no, or `NaN`, value as there is no previous value for it.




In this lesson, the daily returns of different stocks are calculated.

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 2: Daily Returns

Daily returns

Calculating daily returns