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A quick rundown of what to expect from this course, how we're going to move forward, and the roadmap we'll follow as we progress through the course.
Video time: 03m 26s
Let's start downloading the tools we need for the course, including downloading Python itself.
Video time: 03m 47s
We need one more major tool to do data analysis with Python: the Git Bash Terminal. In this video, I'll show you how to download and install it.
Video time: 04m 02s
A virtual environment is a helpful tool that allows us to install programs and try out code without affecting the rest of our computer. In this video, you'll learn how to set up your own virtual environment.
Video time: 04m 12s
We're going to use Jupyter Notebooks throughout the rest of the course to do all our coding, so in this video, let's install and start using it.
Video time: 04m 53s
In this video, we'll download and use our first data analysis tool: Numpy, which stands for Numerical Python.
Video time: 05m 22s
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.
Video time: 03m 16s
In this video, we discuss how Numpy arrays work, including how they are much, much faster to work with than regular Python arrays.
Video time: 04m 28s
Let's discuss mathematical operations we can use in Numpy, including scalars.
Video time: 04m 46s
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.
Video time: 05m 04s
Pandas is a powerful Python data analysis toolkit. In this video, we'll discuss what it does and how it works.
Video time: 03m 29s
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.
Video time: 05m 14s
DataFrames are like spreadsheets, and in this video, we start building and using them.
Video time: 05m 03s
We discuss how to select individual and multiple columns within a data frame.
Video time: 03m 05s
In this video, we cover how to add a new column to your data frame.
Video time: 03m 55s
Let's discuss how to use the drop function to remove a column from your data frame.
Video time: 03m 49s
Deleting a row in a data frame is very similar to deleting a column - but with one small difference.
Video time: 04m 36s
In this video, we'll discuss two different ways to select a row within a data frame, including the loc function.
Video time: 03m 42s
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.
Video time: 04m 06s
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.
Video time: 04m 13s
In this video, we'll explain how to select data based on multiple conditional statements.
Video time: 04m 43s
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.
Video time: 04m 45s
Let's discuss multi-indexes: what they are, how they work, and how to select data from them.
Video time: 06m 04s
You may encounter missing data while doing analysis. What should you do? In this video, we'll discuss some helpful alternatives.
Video time: 06m 15s
The groupby function allows us to group different pieces of data together. We'll discuss the function in this video.
Video time: 03m 51s
There are different ways to combine various pieces of data within your data frame. In this video, we'll discuss concatenating.
Video time: 05m 02s
We discuss how to merge and join data frames together in this video.
Video time: 05m 56s
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.
Video time: 04m 42s
In this video, we discuss how to sort and order your data.
Video time: 03m 40s
In this video, we begin discussing data visualization, and install matplotlib to help us create histograms.
Video time: 04m 44s
We discuss how to create another type of data visualization: area plots.
Video time: 03m 11s
In this video, we explain how to create bar plots using your data.
Video time: 02m 57s
Let's create line plots and discuss some of the options you can use to change their appearance.
Video time: 03m 43s
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.
Video time: 03m 54s
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.
Video time: 04m 32s
Let's start talking about machine learning. In this video, we introduce linear regression and the least squares method.
Video time: 03m 21s
We need to install some more elements to do a linear regression, so we'll discuss what we need in this video.
Video time: 05m 46s
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.
Video time: 03m 56s
In this video, we'll learn how to make predictions and analyze results based on the data analysis we've performed so far.
Video time: 03m 57s
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