Delve into foundational Python programming, explore libraries like pandas and Matplotlib, and gain insights into data management, analysis, and visualization with practical projects.

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Python is one of the most popular programming languages for data science and analytics. It’s used across a wide range of industries. It’s easy to learn, highly flexible, and its various libraries can expand functionality to natively perform statistical functions and plotting.

This course is a comprehensive introduction to statistical analysis using Python. You’ll start with a step-by-step guide to the fundamentals of programming in Python. You’ll learn to apply these functions to numerical data. You’ll first look at strings, lists, dictionaries, loops, functions, and data maps. After mastering these, you’ll take a deep dive through various Python libraries, including pandas, NumPy, Matplotlib, Seaborn, and Plotly. You’ll wrap up with guided projects to clean, analyze, and visualize unique datasets using these libraries.

By the end of this course, you will be proficient in data science, including data management, analysis, and visualization.

Introduction to Data Science with Python

## Introduction
Matplotlib is the most popular plotting library for Python. Its creator, #key# John Hunter: John D. Hunter (August 1, 1968 – August 28, 2012) was an American neurobiologist and the original author of Matplotlib. #key# originally wrote it to visualize the electrocorticography (ECoG) data of epilepsy patients during his post-doctoral research in neurobiology. He created this library to replicate MATLAB’s plotting capabilities in Python. (If you’ve ever worked with MATLAB, you’ll find it natural to work with `matplotlib` will feel natural to you). This open-source library ultimately emerged as the most widely used plotting library for Python and a core component of the scientific Python stack, along with NumPy, SciPy, and IPython.



Along with providing great control on every element in a figure, Matplotlib makes it very easye to work with simple plots. Matplotlib is very customizable in general and, with just a few lines of code, it generates high-quality plots, histograms, power spectra, bar charts, error charts, scatter-plots, and so on.


(Click here to see the [Matplotlib source code on github](https://github.com/matplotlib/matplotlib)) 


# Introduction
Matplotlib is the most popular plotting library for Python. Its creator, #key# John Hunter: John D. Hunter (August 1, 1968 – August 28, 2012) was an American neurobiologist and the original author of Matplotlib. #key# originally wrote it to visualize the electrocorticography (ECoG) data of epilepsy patients during his post-doctoral research in neurobiology. He created this library to replicate MATLAB’s plotting capabilities in Python. (If you’ve ever worked with MATLAB, you’ll find it natural to work with `matplotlib` will feel natural to you). This open-source library ultimately emerged as the most widely used plotting library for Python and a core component of the scientific Python stack, along with NumPy, SciPy, and IPython.



Along with providing great control on every element in a figure, Matplotlib makes it very easye to work with simple plots. Matplotlib is very customizable in general and, with just a few lines of code, it generates high-quality plots, histograms, power spectra, bar charts, error charts, scatter-plots, and so on.


(Click here to see the [Matplotlib source code on github](https://github.com/matplotlib/matplotlib)) 


Let’s learn about plotting some basic plots using matplotlib.

Introduction

Python Essentials

NumPy: Data Analysis

Pandas: Data Analysis

Matplotlib: Data Visualization

Seaborn: Statistical Data Visualization

Data Visualization with Seaborn for Walmart Sales Projection

Pandas: Data Visualization

Data Visualization with Seaborn for Walmart Sales Projection

Plotly: Interactive Data Visualization

Project 1: Oil vs Banks During Recession

Project 2: Emergency Calls Dataset

In Closing

Basic Plotting

Introduction