For those new to coding with Gen AI, this short guide will show you how to start your programming career.
You will learn how to set up and use Anaconda and Jupyter Notebooks to run code that ChatGPT generated for you.
If your data can be uploaded to the cloud, develop in beginner-friendly browser tools such as Lovable, Databutton or Google Colab.
But if you’re working with customer data, you’ll want to process it locally on your machine. Then use a framework like Anaconda so that you can program in Jupyter Notebooks on your own PC.
Getting Started with Anaconda Cloud
Anaconda is a free, open-source distribution of Python and R for scientific computing and data science.
It simplifies package management and deployment, offering over 1,500 packages pre-built and ready for use.
To get started, visit the Anaconda homepage and log in or download the Anaconda Distribution that matches your system requirements.
Once logged in or installed, you can manage libraries and environments with Anaconda’s Navigator, a graphical user interface that allows you to launch applications and easily manage conda packages, environments, and channels without using command-line commands.
But as a beginner, just skip the advanced material and copy someone else’s Jupyter Notebook to run.
Jupyter Notebooks are pre-built packages of code. Don’t expect actual hardware notebooks…
Spinning Up Jupyter Notebooks
Jupyter Notebooks is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.
It’s an invaluable tool for data cleaning and transformation, numerical simulation, statistical modeling, machine learning, and much more.
With Anaconda, you can launch Jupyter Notebooks with a single click from the Navigator.
Writing and Running Code
As a non-technical person, you may not be familiar with Python code.
However, you are familiar with the English language. Just ask a cutting-edge AI service, such as ChatGPT, to generate the code for you in a snap.
Here’s a little sample of mine:
Put your code in Anaconda’s input cells and execute it by clicking the run button. The output will display directly beneath the input cell.
It’s insanely easy!
In my example, I have a Python script that hashes email addresses using SHA-256 – a cryptographic hash function.
This is a common practice in digital advertising to mask personal information before it is submitted for paid Internet advertising.
Making Changes and Debugging
Making changes and debugging your code is straightforward with Jupyter Notebooks.
You can edit the code directly in the cell and rerun it to see the updated results.
The Notebooks interface also provides intuitive tools for debugging, such as inline error messages and the ability to step through code.
Hat tip to Nicolas Glauser of Flin Agency for pointing me in the right direction!
Try GPTs instead of Jupyter Notebooks
While Jupyter Notebooks can be very simple and useful, OpenAI offers an even easier way to create your own AI applications with the GPT Store. Here are my instructions.
So how do you prepare for the opportunities and risks of the coming workplace revolution?
