Welcome to the development of Scatterplot. We encourage you to use this journal in every way possible, by reading, viewing, and commenting on the resources we provide. We hope this will inspire you to engage with us more fully by submitting, reviewing and sharing additional content. We begin this introduction by answering the very basic question of why we started this project, and why we are so excited to see it take shape.
Formally, according to our Aims and Scope, “Scatterplot: The MAA Journal of Data Science serves the mathematics community by providing research and expository content at the intersection of mathematics, data science and educational practice. Suggested topics include pedagogy and teaching practice, development of curricula that combine mathematics and data science at the course or program level, intersections with other disciplines, social justice and ethics, and exposition of mathematical ideas used in data science applications. Teaching approaches that help grow and diversify the population of students in these fields are especially encouraged, as are contributions describing the experience of current students or recent graduates in applications and careers.”
That is a good description of what we plan to do, but it does not include the background of why we feel this work is important and worth the effort. There are many reasons to think we can be of service to the mathematics community.
First, and most important given the mission of the MAA, is that Scatterplot will be of great service to students. Making more explicit connections between mathematics and modern applications can help us grow and diversify the population of students studying mathematics in college. Working data scientists are best prepared when they have knowledge of mathematics, including some specific technical knowledge as well as the more general practice with problem solving and logical thinking skills. The general skills have been part of the mathematics curriculum for many years, and remain in place today, but the specific technical requirements are very different for data scientists than they are for college graduates aiming for graduate study in mathematics. There are many ways to teach and learn key data science concepts, like risk assessment, high-dimensional thinking, and discrete optimization, in innovative ways that may be quite different from the traditional “calculus first” curriculum. By studying and showcasing innovative pathways into upper-level mathematics, we hope that we can be part of the solution to removing barriers to enrollment. In turn, the students we attract will be well positioned to succeed in rewarding careers that provide economic opportunity.
It is also our goal that Scatterplot will serve individual faculty and departments as they look to design and implement new ideas. Our hope is that the material presented will be sufficiently general that many departments can use it, but also that it will be a ‘just right’ fit for mathematicians. There is no lack of places to learn about data science on the internet, and lots of great resources for effective teaching in mathematics, but we want to make it easy to connect these fields. Another important consideration for us is to provide a highly visible outlet for people who are developing innovative curricula, and carefully tracking the outcomes for their students. Our community is doing a lot of great work in this area, and having an opportunity to share and promote that work will be a professional development opportunity for teachers. It is also a nice way to spread that work beyond a single institution. The editorial board will work hard to ensure that Scatterplot maintains the high quality associated with the other outstanding journals produced by the MAA/Taylor & Francis partnership.
We also believe that Scatterplot can in some small way provide service to our society’s well-being. One concern that colleagues have raised about the move toward data science generally and the launching of Scatterplot particularly has been expressed to me in comments like this: “I love the beauty of mathematics, and I do not want my teaching career to be spent doing training for the big corporations that will hire my students.” Those of us involved in this project think this is a false dichotomy. For at least a century, mathematics curricula have ranged from the very traditional pure math preparation for graduate school to very applied orientation for careers, often in actuarial science and engineering. Each institution and department can find their own niche, but we believe that even the most traditional and theoretical programs can find ways to connect their work to modern applications. We aim to provide the resources to make those connections interesting and easy to implement. We also believe that our mathematics students who go on to work as data scientists, or similar roles in business, industry and government, will be best prepared to do so in an ethical and societally valuable way if they have deeply considered the implications of their work, and that our mathematical community is well positioned to encourage this sort of critical thinking.
The paragraphs above provide some rationale for why we have started Scatterplot but we also want to explain what we plan to do. The following are some of our ideas, and we plan to explore examples of many of these in the coming months and years. But we also stand ready to consider other types of content submissions, and we encourage those with creative ideas to suggest other formats. Our topics might include:
Examples of curricular innovation. These could be at the program level or course level. While we welcome review articles that describe an innovation in qualitative terms, we are also very eager to learn about initiatives where thoughtful design and careful tracking of outcomes has informed further development. Encouraging research through evidence-based teaching practices is a way that we can connect our work with other MAA initiatives.
Mathematics and data science in the broader educational context. Those of us who teach at post-secondary schools are always reacting to (and sometimes proactive in shaping) the K-12 curriculum. The experience of colleagues working with government education bodies, private foundations and partner disciplines will be of general interest.
Case studies. These cases will appear with peer reviewed teaching notes to make them more likely to be adopted for classroom use. If particular data sets or repositories are involved, we will work with authors to make these available for use elsewhere.
Conversations with mathematics alumni in the workforce, perhaps as video interviews. We think the community can learn a great deal from young professionals who will report on how their mathematical training shaped their careers. Building stronger ties between mathematicians and employers is one of our priorities.
Student projects in data science that apply mathematical ideas. Student authors or co-authors will be most welcome, and it is easy to imagine contributions where ideas from a mathematics course were instrumental to the success of a student class project, consulting opportunity or internship.
Reviews. We look forward to having community members report on their work teaching or learning from all manner of resources (books, software packages, data sets and repositories) with suggestion for effective application in teaching.
If we do this well, in fact, we will be using good ideas from data science to teach the community about data science. This is an ambitious and challenging goal. We certainly expect to include content that is similar to what might be found in traditional journals, but we also hope to take advantage of technology to have a sense that we are more than just on-line articles that could have all been in print.
I conclude by thanking the large set of people who have worked with us to get this project underway and who have been supportive and patient throughout the process. In the four years since Carol Baxter (MAA Director of Publications) and Michael Pearson (MAA Executive Director) asked me to work with them on this project, we have greatly benefitted from the input of academics, industry experts and reviewers. The members of the editorial board have been vitally important in helping us shape policy and develop content. The MAA staff, particularly Carol, Michael and Bonnie Ponce, have done a wonderful job of keeping the momentum going. Our partners at Taylor & Francis, notably Paul Naish, have been welcoming and enthusiastic. Seeing the first issue come to light is, for me, a professional milestone comparable to finishing my dissertation or receiving tenure. We all know that these accomplishments may have one individual’s name associated them, but they are always in fact the result of many contributions. I am eternally grateful to all of these contributors.
We hope that you enjoy reading Scatterplot, and that you find it useful in helping students make good decisions with data. Please be in touch with questions, suggestions or ideas by writing us at [email protected].
Editor
Babson College, Scatterplot
[email protected]