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.

new.tar.gz

data_science_notebooks

docker_env

data_science_notebooks-copy

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

# 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.

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 ?

Basic Statistics on Numpy Arrays

Basic statistics

`np.mean`