Learn the basics of data science and how to work with important libraries such as NumPy, pandas, Beautiful Soup, etc.

Become a Data Scientist

This module will get us familiar with the state of data science and its fundamental concepts. We'll be going through the applications of data science and a few libraries which are essential to solve any problem in this field, such as Beautiful Soup, Scrapy, NumPy, pandas, spaCy, etc.
We'll also learn about data manipulation with NumPy as well as probability and statistics.
By the end of this module, we'll have basic and essential knowledge of data science and its libraries.

Getting Started with Data Science

# Basic statistics
## `np.mean`

It computes the arithmetic mean along the specified axis of the numpy array. It takes in an `axis` parameter.

* If the axis is not specified, it returns the mean of the flattened version of the array.
* If the axis is specified as zero, it returns the mean across each column.

* If the axis is specified as one, it returns the mean across each row.

## `np.std`

It computes the standard deviation along the specified axis of the numpy array. It takes in an `axis` parameter.

* If the axis is not specified, it returns the standard deviation of the array's flattened version.
* If the axis is specified as zero, it returns the standard deviation across each column.

* If the axis is specified as one, it returns the standard deviation across each row.


## `np.var`

It computes the variance along the specified axis of the Numpy array. It takes in an `axis` parameter.

* If the axis is not specified, it returns the variance of the flattened version of the array.
* If the axis is specified as zero, it returns variance across each column.

* If the axis is specified as one, it returns variance across each row.




Dive into the basic statistics on Numpy arrays and their functions.

Basic Statistics on Numpy Arrays

Basic