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

# Ordering operations
## `np.argmax`
It provides the indices of maximum values along an axis. It takes in a parameter `axis`, which if not specified, the index of maximum entry is returned from the flattened version of the array. If this is specified as zero, the indices of maximum values along the columns are returned and vice versa.

## `np.argmin`

It works the same way as `argmax`, except now the minimum value is returned.


## `np.sort`

It sorts the Numpy array. It takes in a parameter of `kind` to sort from different sorting algorithms like `quicksort`, `mergesort`, and `heapsort`.
It also takes in a parameter of `axis`.
 
* If the axis is specified as `None`, it flattens the Numpy array, and then sorts it.
* If specified as zero, it sorts along the first axis.
* If left blank, the Numpy array is sorted along the last axis by default.


# Coding Exercise





In this lesson, you’ll continue learning about Numpy arrays and their functions.

Sorting Numpy Arrays