Fundamental Data Analysis and Visualization Tools in Python

In this course, you will gain a full understanding of how to utilize Python in conjunction with scientific computing and graphing libraries to analyze data, and make presentable data visualizations.

This course is designed for both beginners with some basic programming experience or experienced developers looking to explore the world of Data Science!

Let’s see a brief overview of the topics covered in this course. We’ll be covering Numpy, Pandas, Matplotlib, Seaborn, and Plotly.

We’ll begin by learning how to create and manipulate arrays with Numpy, which is the core Python package for scientific computing.

Next, we’ll learn the basics of Pandas. We’ll use Pandas to read in data from files, and structure it with Series and DataFrames. We will also learn how to do quick analysis on our data.

We’ll look at how to create various types of charts with Matplotlib, and then customize them to be more attractive and modern with the use of Seaborn.

Finally, we’ll take our charts to the next level by making them interactive using Plotly, which is currently one of the leading data science tools.

At the end of the course, you’ll bring everything you learned together by doing practical exercises using real datasets.

So let’s get started in our journey of learning how to analyze and visualize data.

Learning how to use NumPy is fundamental because it provides a strong base for many other data science and data visualization libraries. The main feature that NumPy provides is a high-performance, multidimensional array object. In this tutorial we're gonna be learning the core tools and functions of working with NumPy arrays such as creating, indexing, and manipulating them.

After watching this video, you will have learned:

• How to create NP 1-dimensional and 2-dimensional arrays

• How to index NP arrays with integers

• How to index NP arrays with booleans

• How to slice NP arrays

• How to use basic NP array math operations

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with labeled data easy. It's fundamental for doing practical data analysis in Python.
Before you jump into the modelling or visualizations, you need to have a good understanding of the nature of your dataset and pandas is the best tool to help you do that.

Core Data Structures

The two primary components that Pandas introduces are the Series and the DataFrame. This video will provide an introduction for how to properly utilize these important tools.

After watching this video, you will have learned:

• How to create Series and Data Frame objects and fill them with data from CSV files

• How to use integers to index Series and Data Frames

• How to use booleans to index Series and Data Frames

• How to convert a dictionary into a Series

• How to select columns/attributes of a Data Frame

• How to group/aggregate data using the groupby function

Matplotlib is a very powerful visualization library that is useful for those wanting to plot data with Python. The most used module of Matplotib is Pyplot which provides a collection of functions that let you easily plot out data.

After watching this video, you will have learned:

• How to make Line Graphs

• How to format your plots using format strings

• How to use NumPy in conjunction with Matplotlib

• How to plot out multiple sets of data on the same figure

• How to make Bar Graphs

• How to make Histograms

Seaborn is a powerful data visualization library that provides a high-level interface to Matplotlib. Seaborn lets you plot attractive charts in a much simpler way. In this section, we'll use a fun Pokemon dataset to explore some of Seaborn's most important features.

After watching this video, you will have learned:

• How to create Scatter Plots

• How to express a third dimension of information in your plots using color

• How to create Box Plots

• How to create Violin Plots

• How to create Heatmaps

• How to create Histograms

• How to create Count Plots

• How to create Density Plots

Plotly is one of the leading data visualization libraries for Python. It ships with over 30 chart types, including scientific charts, 3D graphs, statistical charts, financial charts, and more. In this tutorial, we will be implementing the same plots we saw in the last tutorial using Seaborn, however, using Plotly we will make them interactive.

After watching this video, you will have learned:

• How to use the various Plotly UI tools such as hover, zoom, pan, select, and more.

• How to create interactive Scatter Plots

• How to resize and color points on a chart according to attributes in the data

• How to add information to the hover data of each point on a chart

• How to see the summary values of Box Plots and Violin Plots

• How to see correlation values with an interactive Heatmap

• How to make interactive Histograms to see the amount of values in a range

• How to make interactive Density Heatmaps