Data Visualizations with Python

To successfully complete the course, you’ll need to have experience with the programming language Python. You’ll also need to have experience with tools and software such as Excel and Jupyter Notebook, and experience working with statistical algorithms. You should be comfortable using many of the common libraries for working with data, for instance, Pandas, NumPy, and Seaborn. It’s also necessary to have practiced executing basic Python commands in Jupyter Notebook or a similar tool, such as Google Colab, nteract, VS Code, or JupyterLite.

Additionally, you’ll need:*

• Interest in machine learning
• Written and spoken English proficiency at a B2 level or higher
• A computer (macOS, Windows, or Linux) with a webcam, microphone, and an internet connection.

*Note: You will be required to invest some independent study time (approximately 1-2 hours per week) towards familiarizing yourself with the tools you’ll use throughout the course, and learning how to use them.

You’ll be using the tools; Jupyter Lab, GitHub, Anaconda, and Python. You'll also use a Word Processing tool and a Presentation tool of your choice.

All the tools and software you’ll need are free to use—with no additional cost to you.

Compatible operating systems: Windows 11, macOS versions 10.13 and later, Ubuntu, Debian, CentOS, or Fedora (Linux).

Questions? Contact us for more information on requirements for your specific operating system.

Yes, the course is entirely asynchronous and online—so you can study when and wherever you’d like so long as you can get online and complete the course on time.

But this doesn’t mean the learning experience is isolated or lonely! You’ll have your mentor, tutor, and student advisor there to support you—as well as access to our active student community on Slack.

We take a rigorously practical approach to learning. You’ll have the opportunity to apply everything you learn in practical ways. Every exercise builds up to a completed portfolio project that your mentor will review and that will show employers the in-demand skills you learn in the course.

If you set aside 15-20 hours per week to study, you’ll complete the course in approximately two months (eight weeks). If you’re able to devote 30-40 hours per week, you can complete the course in about a month (four weeks).

This course offers immersive training in the field of data visualization—including expert-authored curriculum, hands-on projects, and personalized mentorship and support from experts in the field. Everything you need to stand out in the field as the specialist you’ll be.

Find out more here:

• How it works: From the curriculum to your support team, and beyond—here are the details.
• Meet our mentors: Get to know who the CareerFoundry mentors are and how the dual-mentorship model works.
• Graduate outcomes: Here’s some of the work our graduates did in the full program—and where they’re at today.

Yes, we offer two payment options for your specialization course. First, you can save a little money by paying your full tuition up front. If that’s not feasible, you can pay a set amount up front (varies depending on currency), and then the remainder in three monthly payments.

While the course is not university accredited, it does undergo a rigorous quality assurance and certification process with the ZFU (Staatliche Zentralstelle für Fernunterricht)—the state body for distance learning in Germany.

This process ensures that the course meets a high stand for an excellent and effective learning experience.

On successful completion of this certification process, the course is assigned a unique approval number (7443623) which can be checked against a public register.