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

Seaborn provides a range of options to plot categorical data. Seaborn divides categorical plots into three groups:

**Categorical scatter plots:** These show each observation at each level of the categorical variable. They include the following:
* `stripplot()`
* `swarmplot()`

**Categorical distribution plots:**  These apply a statistical estimation to show a measure of central tendency and confidence interval. They include the following:
* `boxplot()`
* `violinplot()`
* `boxenplot()`

**Categorical estimate plots:** Apply a statistical estimation to show a measure of central tendency and confidence interval.
* `barplot()`
* `countplot()`
* `pointplot()`

Along with the types of categorical plots mentioned above, we’ll also discuss a different kind of plot— `catplot()` (previously known as factorplot).

Let’s learn about categorical data and their plotting options.

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

Plotly: Interactive Data Visualization

Project 1: Oil vs Banks During Recession

Project 2: Emergency Calls Dataset

In Closing

Categorical Plots