The trust in statements made by politicians and other public figures is at an all-time low (Economist, 2017). This may be due to a real increase in the number of false claims made (or “alternative facts” cited), or a side-effect of the low affinity to the truth displayed by the current white-house incumbent (and other leaders around the globe). At the same time, the polarization of the media undermines its credibility as an unbiased fact-checker (The Hill, 2017; American Press Institute, 2017). The good news, on the other hand, is the unprecedented ability of every one of us to independently fact-check claims we are interested in. With plethora of raw data freely available on the internet and accessible tools for data analysis, all we need is to know how to ask the right questions, how to translate these questions into executable programs, and how to implement these programs and interpret their results.

In the age of blunt lies and accessible information, we believe that such skills should be acquired as early as the high-school years. We posit that the way to develop these skills is through hands-on experience, and that the way to motivate students to gain this experience is to let them ask and answer the questions that interest them. With this in mind, we developed an interactive textbook (Biron & Levine, 2019) written completely as a collection of Jupyter Notebooks. The book teaches fundamentals of data science through real-life fact-checking tasks, defined by the reader according to their own personal interests. Our goal is to teach three types of skills: how to design a fact-checking plan, how to retrieve the appropriate data, and how to analyze them. The Jupyter platform allows us to let the reader choose the questions they address and the data sets they use to engage in active learning, guaranteeing that the book is relevant and exciting to everyone, whatever their interests may be.

In this talk I’ll describe our mission to make students – young and old – into budding data scientists, equipped with the methodology, design patterns, and Python, the language of data science. I will also discuss how platforms like Jupyter and Colaboratory provide with tools to revolutionize our teaching and help us adapt our methods to the students of Gen Z.