Part I. Jupyter Notebook Set-up
Let's keep it simple by using Anaconda a free data science tool kit that combines most of the popular data science tools in one easy to use desktop application. It has great support and is easy to install.
1.) Download Anaconda Individual Edition
2.) Select the Graphical Installer
3.) Run the installer by double-clicking it
Press continue for each of the steps and then press 'Install'.
4.) Verify that Anaconda has been installed by starting the application
Find where the application was installed (usually the Applications folder) and then double-click the app to launch it.
5.) Launch Jupyter Notebook
Once the app has launched you should see a view that looks like this.
Understanding Jupyter Notebook: 20,000 ft View
When you launch Jupyter Notebook a terminal (black box) should launch because it is starting a local server on your computer.
A server is a chunk of code that runs in a loop to support an application.
You'll also see your default browser open and a new tab (white box) which shows your file system from the root and all the files within it.
Now, go to my GitHub and download the code for the repo.
After downloading the zip file open it and there will be two Jupyter Notebooks
Now, go back to your Jupyter notebook tab or relaunch it if you've closed it. Then go to wherever you saved the code (probably downloads).
Part II. Learn the Basics of Jupyter Notebook
Double click '1_Introduction_to_Jupyter_Notebook.ipynb' and you should see the following
The above notebook is an introduction to Jupyter Notebooks and shows what kind of things can be done inside of it. The Notebook interface is an incredibly common way for data scientists and data analyst to explore and manipulate data.
Because inside of the notebook is a language interpreter, in this case Python, but if DataBricks were used then a plethora of other popular data science languages could be used like Scala.
For now let's stick to Python.
Part II. The Python Basics
Now, that you've gotten used to Jupyter notebooks go ahead and open the other notebook included in GitHub '2_introduction_to_basic_python_programming' which contains the basics of what you'll need to know to get started with Python.
The notebook is the same one I used in my first Machine Learning class at the University of Houston. A big thanks to the great professor Jiajia Sun of the Geophysics Department.
Take your time with the notebook and get comfortable enough with Python to code most the exercises from memory.
After you've gotten comfortable check out my post 30 days of posting to TikTok : I share the exact steps of how I used Machine Learning to make my most successful video In it I provide a real world example of using data analytics to do something cool like figuring out how to get more engagement for my TikTok account.
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