Creating an Anaconda Account and Opening Your First Notebook

Before you start building models in Python or R, you need astable environment—here’s how Anaconda helps you manage dependencies and get coding quickly.

Learning how to write code and build machine learning models is hard enough. However, making sure you have all the right libraries and dependencies makes it even harder. When I started learning to write R and Python code, issues with my libraries not aligning correctly were the biggest culprits behind my urge to throw my computer through the window.

All libraries have dependencies, or another library (often many libraries) that they’re dependent on to work correctly. This gets really complicated when libraries have updates with new features, bug fixes, and soon. There’s often a range of compatible versions of a specific package that fulfill the package dependency. Across a large number of libraries, this gets very complex and challenging to manage.

What Is Anaconda?

Anaconda is a cloud-based platform data scientists use to develop and run code. The magic of a platform like Anaconda is that it will manage these dependencies for you—a behind-the-scenes feature that’s so incredibly useful. Without this magic, libraries are just folders and files in a directory, and it’s all on you to understand the dependencies.

How to Create an Anaconda Account

Start by Googling “Anaconda.”

Click on the Anaconda link and you’ll arrive at their home page that prompts you to create an account, as shown here.

After creating your account, navigate to the Notebooks tile, as shown in this figure.

The Notebooks tile will open up a Jupyter Notebook for you to start writing your code, as shown below.

Jupyter Notebook is one of the standard IDEs used by people writing Python code. As you’ll see when you start writing code, it’s broken into code chunks, which are blocks of code that allow you to only run one part of your code at a time and then see the result of the code. Even if it’s not clear to you now, you’ll see how helpful this way of writing code can be when you’re learning.

At the time of writing, Anaconda includes a helpful README file that you can reference if you’re interested in learning more about Anaconda notebooks.

Conclusion

With your Anaconda account set up and a notebook open, you’re ready to begin writing and running code in a controlled environment that manages libraries and dependencies for you. Instead of spending time troubleshooting version conflicts, you can focus on learning core concepts and building your skills in Python or R. As you continue, you’ll see how valuable astable, well-managed environment can be, especially as your projects grow more complex.

Editor’s note: This post has been adapted from a section of the book Applied Machine Learning: Using Machine Learning to Solve Business Problems by Jason Hodson. Jason has worked in data-centric roles for nearly a decade. He currently works as an HR analytics manager, and he has prior experience in a forecasting role using the full range of applied machine learning. In a previous role, Jason wrote the end-to-end code for an enterprise hiring manager and candidate experience process, collaborating with recruiting leaders to understand and leverage data from a company-wide survey. He’s built large data models and dashboards and taught nontechnical users how to adopt and use them. Jason has been a technical mentor in all his roles, helping others develop their analytics and programming skill set. The common thread across Jason’s career is his ability to be a translator for stakeholders, peers, and junior team members. His learning journey also gives him a unique perspective: Before earning a master’s degree in business analytics, he was entirely self-taught. This has made his approach to teaching more practical, allowing concepts to translate better (and faster) into the business world.