Introduction to Data Analysis with Python

In this video, we'll download and use our first data analysis tool: Numpy, which stands for Numerical Python.

Before we start coding with Numpy, let's discuss what it does and how it works. This video will help you understand why Numpy is so important for data analysis.

In this video, we discuss how Numpy arrays work, including how they are much, much faster to work with than regular Python arrays.

Let's discuss mathematical operations we can use in Numpy, including scalars.

Numpy has universal functions built into it that we can play with and use. In this video, we'll discuss square roots, absolute values, exponents, and more.

Pandas is a powerful Python data analysis toolkit. In this video, we'll discuss what it does and how it works.

Pandas series is one of the main "workhorses" of Pandas. We'll discuss how series work and some of the helpful ways you can use them.

We discuss how to select individual and multiple columns within a data frame.

In this video, we cover how to add a new column to your data frame.

Let's discuss how to use the drop function to remove a column from your data frame.

Deleting a row in a data frame is very similar to deleting a column - but with one small difference.

In this video, we'll discuss two different ways to select a row within a data frame, including the loc function.

Let's discuss how to select specific points within your data frames, such as the intersection of a row and column, or multiple points within different rows and columns.

Selecting data based on conditionals - such as greater than, less than, equal to, not equal to - is an essential part of data analysis. We'll discuss that in this video.

In this video, we'll explain how to select data based on multiple conditional statements.

There are times when you may need to reset your index within a data frame, so we'll explain how that works in this video.

Let's discuss multi-indexes: what they are, how they work, and how to select data from them.

You may encounter missing data while doing analysis. What should you do? In this video, we'll discuss some helpful alternatives.

The groupby function allows us to group different pieces of data together. We'll discuss the function in this video.

There are different ways to combine various pieces of data within your data frame. In this video, we'll discuss concatenating.

We discuss how to merge and join data frames together in this video.

In this video, we discuss how to find unique values within a column and how to find value counts as well.

The apply method allows us to create our own functions and apply them to the data in our columns.

In this video, we discuss how to sort and order your data.

We discuss how to create another type of data visualization: area plots.

In this video, we explain how to create bar plots using your data.

Let's create line plots and discuss some of the options you can use to change their appearance.

Scatter plots are a bit more complicated, but can be helpful ways to visualize your data.

In this video, we discuss how to create your own box plots.

In this video, we explain what a hexplot is and how to create your own.

In this video, we discuss density plots, kernel density estimation (KDE) plots, and how to create them.

We need to install some more elements to do a linear regression, so we'll discuss what we need in this video.

As we begin to set up our linear regression model, we must define testing and training splits.

In this video, learn how to train a linear regression model and how easy it is to fit the model.

We'll take a look at the coeffecients and intercepts we discovered at the end of the last video and explain what they mean.

In this video, we'll learn how to make predictions and analyze results based on the data analysis we've performed so far.