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 array manipulation operations

### `np.array_split`
 It is used to split a Numpy array into multiple sub-arrays.

### `np.column_stack`
It is used to stack multiple 1-D Numpy arrays as columns in a 2-D array.

### `np.concatenate`

It is used to concatenate numpy arrays along an axis. There's more on this in the coding snippet below.

### `np.hsplit`

It splits a single Numpy array into multiple sub-arrays horizontally.

### `np.hstack`
It stacks the arrays horizontally in a *column-wise* manner.

### `np.squeeze`
It removes single-dimensional entries from the shape of an array.

### `np.vsplit`
It splits an array into multiple sub-arrays vertically (row-wise).

### `np.vstack`
It stacks multiple arrays in a vertical sequence.


## Coding Exercise




# Numpy array manipulation operations

## `np.array_split`
 It is used to split a Numpy array into multiple sub-arrays.

## `np.column_stack`
It is used to stack multiple 1-D Numpy arrays as columns in a 2-D array.

## `np.concatenate`

It is used to concatenate numpy arrays along an axis. There's more on this in the coding snippet below.

## `np.hsplit`

It splits a single Numpy array into multiple sub-arrays horizontally.

## `np.hstack`
It stacks the arrays horizontally in a *column-wise* manner.

## `np.squeeze`
It removes single-dimensional entries from the shape of an array.

## `np.vsplit`
It splits an array into multiple sub-arrays vertically (row-wise).

## `np.vstack`
It stacks multiple arrays in a vertical sequence.


# Coding Exercise



What is Data Science?

Applications of Data Science

Overview of Libraries

Data Manipulation with NumPy

Data Analysis with pandas

Probability and Statistics

Conclusion

Numpy Array Manipulation

Numpy array manipulation operations

`np.array_split`

`np.column_stack`