The current research landscape has seen an undeniable increase in data-intensive science, consistent with the impact of "big data" in every corner of our society. Within this arena, high volume, heterogeneous, and increasingly real-time data has forced seasoned scientists and engineers alike to adapt intelligent systems techniques into their data life cycle analysis. For educators, administrators and employers, the pervasiveness of datasets across disciplines has unraveled a new set of challenges in an age of shrinking degree plans and rising tuition costs: how can we best teach computational skills to non-computer science students, and how can we accelerate and facilitate the teaching and learning process? These are especially relevant topics within the geoscience community, which has lagged behind other science communities in adopting emerging technologies for big data; more so given the reliability and maturity of multi-sensor and multi-instrumentation networks across the globe. In this research, we seek to summarize the current state of efforts within the earth science community addressing this need, and synthesize findings from other communities regarding data science competencies and approaches to reducing this knowledge gap for students. Incorporating these skills into curricula for our students will ultimately enhance their competitiveness in the workforce.