Gain insights into data science fundamentals, explore machine learning and big data, and delve into real-time projects. Discover systematic approaches to data acquisition, wrangling, and solving diverse problems.

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There is a lot of dispersed, and somewhat conflicting information on the internet when it comes to data science, making it tough to know where to start. Don't worry. 

This course will get you familiar with the state of data science and the related fields such as machine learning and big data. You will be going through the fundamental concepts and libraries which are essential to solve any problem in this field. 

You will work on real-time projects from Kaggle while also honing your mathematical skills which will be used extensively in most problems you face. You will also be taken through a systematic approach to learning about data acquisition to data wrangling and everything in between. This is your all-in-one guide to becoming a confident data scientist.

An Introductory Guide to Data Science and Machine Learning

# 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.

What is Data Science ?

Applications of Data Science

Overview of Libraries

Probability and Statistics

Machine Learning Part-1

Machine Learning Part-2

Machine Learning Part-3

Deep Learning

Machine Learning Tools and Libraries

Big Data Tools and Technologies

Where to go next ?

Sorting Numpy Arrays

Ordering operations