[12/3/14 update: See also this December 2014 Amstat News article by Michelle Schwalbe: Training Students to Extract Value from Big Data.]
The National Academies' Committee on Applied and Theoretical Statistics (CATS) released last week a summary of its popular April 2014 workshop, "Training Students to Extract Value from Big Data." You can view the video of the workshop here.
Co-chaired by John Lafferty of the University of Chicago and Raghu Ramakrishnan of the Microsoft Corporation and funded by the National Science Foundation, the workshop was convened to discuss how best to train students to use Big Data. As explained in the summary's introduction, the workshop explored four topics:
- The need for training in big data.
- Curricula and coursework, including suggestions at different instructional levels and suggestions for a core curriculum.
- Examples of successful courses and curricula.
- Identification of the principles that should be delivered, including sharing of resources.
The interest in the workshop and the large number of downloads of the summary point to both the quality of the workshop and the popularity of the topic. The organizers invited a diverse range of academic and private sector speakers from across the computer science, statistics, and broader data science community. There were also domain scientists (e.g., astronomers) participating. A common theme was the importance of skills from a range of disciplines for addressing big data problems. Indeed, in the concluding panel, one speaker said it would be a mistake for statisticians to develop a data science curriculum without the engagement of computer scientists just as it would be a mistake for computer scientists to develop a data science curriculum without statisticians.
While there was some external criticism of the workshop for the number of statisticians being a minority among the speakers, I thought this was a smart decision as non-statisticians speaking to the importance of statistics in Big Data/data science makes for a more powerful message.
The workshop summary is organized around six chapters:
- Introduction
- The Need for Training: Experiences and Case Studies
- Principles for Working with Big Data
- Courses, Curricula, and Interdisciplinary Programs
- Shared Resources
- Workshop Lessons
Like all National Academy workshop summaries, this report did not make any recommendations based on the workshop.
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