Get started with the basics of analytics in Python, the language of choice for data science.

Python Data Analysis and Visualization

Get started with the basics of analytics in Python, the language of choice for data science using Python data structures,  descriptive statistics, and much more.

Data Analysis and Visualization

## Why Python for analytics?

Python has quickly become the language of choice when it comes to data analytics, machine learning, and data science.

Other languages provide value in this space as well, such as R and Julia. That being said, in my opinion, Python is the clear leader.

In fact, in May 2019, R fell off [Tiobe index's list of the 20 most popular languages](https://www.zdnet.com/article/r-vs-python-rs-out-of-top-20-programming-languages-despite-boom-in-statistical-jobs/), having spent three years in the top tier while Python sat at number 4 with the most significant growth of any language in the top 20.

Python has been so popular because of its applicability to many fields and purposes, its ease of use, and its amazing ecosystem of libraries that support data analytics and machine learning.

We will be using Python exclusively in this course and will discuss many of its great libraries, such as `Numpy`, `Scipy`, `Pandas`, and `Seaborn`.

# Why Python for analytics?

Python has quickly become the language of choice when it comes to data analytics, machine learning, and data science.

Other languages provide value in this space as well, such as R and Julia. That being said, in my opinion, Python is the clear leader.

In fact, in May 2019, R fell off [Tiobe index's list of the 20 most popular languages](https://www.zdnet.com/article/r-vs-python-rs-out-of-top-20-programming-languages-despite-boom-in-statistical-jobs/), having spent three years in the top tier while Python sat at number 4 with the most significant growth of any language in the top 20.

Python has been so popular because of its applicability to many fields and purposes, its ease of use, and its amazing ecosystem of libraries that support data analytics and machine learning.

We will be using Python exclusively in this course and will discuss many of its great libraries, such as `Numpy`, `Scipy`, `Pandas`, and `Seaborn`.

This lesson will describe why we are using Python for this course.

What is Analytics

Python Basics for Analytics

Describing Data

Cleaning Data

Visualizing Data

Analytics in Python

Why Python for analytics? #