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

# Indexing for a 1-D array
Just like normal arrays, elements of a `NumPy` array can also be accessed and changed through indexing. Through `NumPy` indexing, we can also access and change elements in a specific range.

The following code snippet provides an example of all these functionalities:

import numpy as np

arr = np.arange(0,10,1) # Generate array with numbers from 0 to 9
print("The Array")
print(arr)

print("\nElement at index 5")
print(arr[5]) # Fetch element at index 5

print("\nElements in a range of 0 to 6")
print(arr[0:6]) # Fetch elements in a range

arr[0:6] = 20 # Assign a value to a range of elements
print("\nNew array after changing elements in a range of 0 to 6")
print(arr)

> **Note:** The `:` inside the `[]` defines the range. The value to the left is the starting index and is inclusive. Meanwhile, the value on the right defines the ending index, which is exclusive; exclusive means that the value at the end index will not be considered for the resultant.

## Slicing 1-D array
Slicing an array means taking elements from it in a specific range and moving them to another variable. The concept is the same as `string` slicing, but here an array will be sliced. Let's look at an example:

In this lesson, array indexing in NumPy 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

NumPy Array Indexing

Indexing for a 1-D array