Forward

Special thanks to Michael Pearson, Executive Director of the MAA, for being willing to contribute the following Forward to the text, and letting us publish it online as well. He is uniquely positioned to make the case for the importance of this text at this time.

As we often hear, we live in an era where data can be collected, stored, and processed at an unprecedented (and rapidly accelerating) scale. Whether or not that happens in a way that can properly be called science, however, is a critical issue for our society.

The recently-concluded Roundtable on Data Science Postsecondary Education, held under the auspices of the Board on Mathematical Sciences and Analytics at the National Academies, brought together representatives from academia, professional societies, industry, and funding agencies to share perspectives on what tools our students need to participate in this space. Throughout these discussions, it was clear that, while there is no single model for what constitutes data science, there are a wide variety of tools from mathematics, statistics, and computer science which are essential ingredients for anyone interested in exploring this rapidly-evolving discipline.

For those of us in mathematics, it is essential that we become better-informed about the role of our discipline in this emerging field. Doing so will help prepare our students for careers which will more and more depend on some level of competency in understanding how to use data to inform decisions, regardless of the specific discipline or industry in which they find themselves. It can also bring heightened awareness of the importance of rigorous mathematical perspectives to the enterprise.

I believe it is an ethical, and in fact an existential, imperative for the mathematical sciences community to develop a deeper understanding of the role of our disciplines in data science, and to change our educational programs to enable our students to engage with data effectively, and with integrity.

Nathan Carter and his colleagues have made an important contribution by providing an overview of many of the key tools from the mathematical, statistical, and computational sciences needed to succeed as a data scientist, written specifically for those of us in the mathematical sciences -- faculty interested in learning more about data science themselves, graduate students, and others with a reasonable level of mathematical maturity. From my perspective, for those of us concerned with undergraduate mathematics education, it cannot have come too soon.