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

We’ve already seen how to do basic plotting. However, it’s generally recommended to use an object-oriented approach because it allows us much more control over our plots, as well as more customization options.<br> 

Let's break down and learn the formal introduction of [Matplotlib's object-oriented API](https://matplotlib.org/gallery/index.html#api-examples)  for plotting data.<be>

The idea behind the object-oriented approach is that we create figure objects and then call methods or attributes from that object. This elegant approach is helpful when we are dealing with a canvas that has multiple plots on it.

To start, let’s create a figure instance
 `fig1` and add axes to that figure:

Let’s learn about the object-oriented approach in 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

Matplotlib Object-Oriented Approach