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.

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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

So far, we have discussed the basics of `NumPy`. Now, these techniques will be applied to data along with some advanced functions to see what more can be achieved.
# `NumPy` array processing functions


Now, let's discuss some `NumPy` functions that help in array processing.

## NumPy `where` function

Let's look at an example of the `where` function and try to understand it.

import numpy as np
# Declare 2 arrays
arr1 = np.array([10, 20, 30, 40])
arr2 = np.array([500, 600, 700, 800])

cond = np.array([False, True, False, True]) # Create an array with bool operators

res = np.where(cond, arr1, arr2) # apply the where condition
print(res)

What exactly happened here? The NumPy `where` function took 3 values `cond`, `arr1`, and `arr2`. This function first checks the value for `cond`. If the value of `cond` is `True`, then it returns the value of `arr1` for the respective index, and if it is `False`, then it returns the value of `arr2` for its respective index. In this case, `arr1` is the first parameter after `cond`, and `arr2` is the last parameter of the `where` function. These arrays can be changed and replaced with any desired value.

This function prevents us from writing complex nested `if` statements and provides an easy alternative.


Some more NumPy functions for array processing are given below.

| Function    | Description    |
| --- | --- |
| `np.random`    | Generates an array with random numbers    |
| `np.mean`    | Returns mean of all elements in the array    |
| `np.var`    | Returns the variance of all elements in the array    |
| `np.std`    |Returns the standard deviation of all elements in the array     |
| `np.sum`    | Returns the sum of all elements in the array    |
| `np.sort`    | Returns a sorted copy of an array    |
| `np.unique`    | Returns array with all unique elements    |
| `np.any`    | Returns `True` if any element of boolean array is `True`    |
| `np.all`    | Returns `True` if all elements of boolean array are `True`    |

The following example makes use of all these functions.

In this lesson, you will explore some more NumPy array processing techniques.

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

Processing NumPy Arrays

`NumPy` array processing functions

NumPy `where` function

Predictive Data Analysis Exam

Processing NumPy Arrays

NumPy array processing functions

NumPy where function

`NumPy` array processing functions

NumPy `where` function