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

# Series
`Series` can be described as a single column of a 2-D array or a matrix. It has specific index values attached to each row for identification. It can also be imagined as one column of an excel file.

The index values are automatically defined for each `Series` when it is created. These values can also be explicitly defined.
Let's look at an example to understand this:

# importing pandas in our program
import pandas as pd

# Defining a series object
srs = pd.Series([1,2,3,4,5])

# printing series values
print("The Series values are:")
print(srs.values)

# printing series indexes
print("\nThe Index values are:")
print(srs.index.values)

The `srs.values` function on __line 9__ returns the values stored in the `Series` object and the function `srs.index.values` on __line 13__ returns the index values.
# Defining indexes
In the above example, only series values were defined, but its indexes were automatically generated. Each index corresponds to its respective value in the `Series` object. However, explicitly defining the indexes can be of great help if every value needs to be identified with something meaningful.

Let's look at an example where the population growth rate of different countries is assigned to the country name.

In this lesson, the series object of pandas is explained

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

Series in Pandas

Series #