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

## Plotly and Cufflinks

There are a number of options for creating graphics in Python. In recent years, a lot of work has gone into developing tools to create interactive graphics. [Plotly](https://plot.ly/python/) and [Bokeh](https://bokeh.pydata.org/en/latest/) are commonly used tools for this purpose in Python. Of the two, Plotly is the more popular choice among programmers. It’s also easier  to use.



> Plotly is an interactive visualization library. [Cufflinks](https://plot.ly/ipython-notebooks/cufflinks/) binds Plotly directly to pandas dataframes.

Before we learn how to use Plotly, let’s do some imports.

>**Note:** We’re going to do some `plotly` offline imports. First, make sure that your `plotly` version is up to date. For offline plotting, we’ll need version 1.9.4 or above. If you’ve installed the current version of this library, you’re good to go.

# Plotly and Cufflinks

There are a number of options for creating graphics in Python. In recent years, a lot of work has gone into developing tools to create interactive graphics. [Plotly](https://plot.ly/python/) and [Bokeh](https://bokeh.pydata.org/en/latest/) are commonly used tools for this purpose in Python. Of the two, Plotly is the more popular choice among programmers. It’s also easier  to use.



> Plotly is an interactive visualization library. [Cufflinks](https://plot.ly/ipython-notebooks/cufflinks/) binds Plotly directly to pandas dataframes.

Before we learn how to use Plotly, let’s do some imports.

>**Note:** We’re going to do some `plotly` offline imports. First, make sure that your `plotly` version is up to date. For offline plotting, we’ll need version 1.9.4 or above. If you’ve installed the current version of this library, you’re good to go.

Let’s learn about interactive plotting using Plotly.

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 and Common Plots

Plotly and Cufflinks