As data mining research and applications continue to expand in to a variety of fields such as medicine, finance, security, etc., the need for talented and diverse individuals is clearly felt. This is particularly the case as Big Data initiatives have taken off in the federal, private and academic sectors, providing a wealth of opportunities, nationally and internationally. The Broadening Participation in Data Mining (BPDM) workshop was created more than 7 years ago with the goal of fostering mentorship, guidance, and connections for minority and underrepresented groups in the data science and machine learning community, while also enriching technical aptitude and exposure for a group of talented students. To date it has impacted the lives of more than 330 underrepresented trainees in data science. We provide a venue to connect talented students with innovative researchers in industry, academia, professional societies, and government. Our mission is to facilitate meaningful, lasting relationships between BPDM participants to ultimately increase diversity in data mining. This most recent workshop took place at Howard University in Washington, DC in February 2019. Here we report on the mentoring strategies that we undertook at the 2019 BPDM and how those were received.

As data science research continues to expand into a variety of applied fields, the need for talented and diverse individuals has been widely acknowledged. Despite this acknowledgement, data science lags behind other STEM disciplines in achieving a diverse workforce. Through work we have undertaken in the past as part of the Broadening Participation in Data Mining workshop (BPDM) and our work with ACM SIGKDD, we seek to build a better workforce that is positioned to address the data science problems of the next hundred years. A significant barrier to trainee long-term career success is their limited ability of underrepresented trainees to demonstrate their analytical abilities and sophisticated inferential talents to address key data issues in our community. In this talk we will present an overview of the goals of the Diversity and Inclusion track and share our vision for how we bridge this diversity divide that our society and our data science workforce needs right now. We are interested in how diversity is encountered across ethnic, gender, and ability identities. To this end we have prepared an exciting new program activities to facilitate broader conversations in the data science field that cover not only technical ideas but innovative thinking in what the future of data science can look like if we diversify the group of contributors and enlarge those included.