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

# NumPy

Data comes in various forms and needs to be stored and processed in Python programs. NumPy is meant for such purposes and enables a person to operate on data in an optimized and fast way. It handles matrices and multi-dimensional arrays very efficiently. It is used extensively for linear algebra tasks. It comes under the umbrella of modules that are meant for scientific purposes in Python.  In this lesson, we'll understand this using various hands-on exercises and learn how it works.

## `ndarray` data structure

`ndarray` is short for N-dimensional array. It has a fixed size in memory and contains elements of the same type. We will understand the operations that are extensively used in the industry on NumPy arrays.

# Difference between Python list and NumPy array

All the elements in a NumPy array are homogeneous, meaning of the same type. NumPy provides a wide variety of ways to create arrays. Elements in a Python list can be of different types. This gives the following advantages to NumPy arrays over Python lists:

* Mathematical functions over the homogeneous numpy arrays are extremely fast.

* Numpy arrays consume less memory. Their data types are defined, which makes them more optimized than a standard Python list.

# Basic operations and manipulations with NumPy arrays

Now, we'll dive into extensive hands-on work with NumPy arrays and understand the operations used extensively in the industry and day-to-day lives of data scientists.

## Using different attributes

In the code snippet below, we will look at the most commonly used attributes of NumPy arrays.

* `ndarray.ndim`: This is used to get the number of dimensions of an `ndarray`.

* `ndarray.shape`: This gives us the tuple showing us the size of each dimension of a Numpy array.

* `ndarray.size`: This gives us the total size of the ndarray and is the multiplication of elements in the tuple obtained by `ndarray.shape`.

* `ndarray.dtype`: This gives the datatype of the ndarray.

* `ndarray.itemsize`: This gives us the size in bytes of each element of the ndarray.

* `numpy.reshape(a, newshape, order='C')`: It gives us the ndarray with the same data but with a new shape. Parameter `a` is the ndarray that is to be reshaped. 

* `numpy.amin(a, axis=0), numpy.sum(a, axis=0), numpy.amax(a, axis=0)`: As the names suggest, these give us the minimum value, the sum of values, and the maximum value of the `ndarray`, respectively. Parameter `a` is the `ndarray` on which the respective operation is to be applied. Specifying the parameter axis gives us the control to either perform the operation row or column. `axis=0` performs the operation column-wise, and `axis=1` performs the operation row-wise.


* `numpy.cumsum(a, axis=0)`: This gives us the cumulative sum along the specified axis. Parameter `a` is the `ndarray` on which the respective operation is to be applied. 

* `numpy.ravel(a, order='C')`: This gives us the flatten(1-D) version of the ndarray. Parameter `a` is the `ndarray` on which the respective operation is to be applied. 

* `ndarray.astype(dtype)`: This casts the copy of the array to a specified type. 


Let's jump into practical stuff, incorporating the above attributes and functions, and many more used extensively in the field.

# Coding exercise




Learn about NumPy, an acronym for Numerical Python, which provides multi-dimensional arrays along with functions to operate on them in this lesson.

Numpy Basics

NumPy

ndarray data structure

Difference between Python list

`ndarray` data structure