mlxtend.tar.gz

visualization

This course assumes no prior experience and can teach anyone how to use Python to analyze and visualize data. Stop using Excel and start harnessing the power of Python.

Data analysis is one of the hottest careers of the 21st century. As an analyst, your goal is to peel back layers of data in order to answer questions of interest; that is the power of analytics. It allows you to take raw data and create meaningful, actionable insights.

In this course, you’ll learn how to use Python, NumPy, SciPy, Pandas, and Seaborn to perform data analysis and visualization. You’ll explore the four crucial steps for any data analysis project: reading, describing, cleaning, and visualizing data. In each step, you will work with the most common and popular tools that data analysts use every day. By the end of the course, you will be able to confidently extract knowledge and answers from data.

Python 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

Reading Data

Describing Data

Cleaning Data

Visualizing Data

Analytics in Python

Why Python for analytics? #