Growing interest in Big Data is leading industries, academics and governments to accelerate Big Data research. However, how teachers should teach Big Data has not been fully examined. This article suggests criteria for redesigning Big Data syllabi in public and private degree-awarding higher education establishments. The author conducted a survey of 35 Big Data syllabi across different academic institutions in the USA using Palmer, Bach, and Streifer's rubric criteria. The role of syllabi in higher education has an established tradition in summarising topics covered in a single course and textbook, and in referencing the instructor's resources. Yet, despite the central role of course resources, the present study did not find a common textbook. The majority of resources referenced were academic articles and blog postings used by the instructors and other professionals in the field. Based on Palmer et al.'s score rubric, this study found that many of the syllabi broke down the main ideas of Big Data into smaller content items using interpretations of the instructor's subject knowledge. The study recommends that Big Data instructors need to provide a better breakdown of each component of the syllabus to reflect a clear understanding of grades and resources available on the subject. Future studies also need to examine students' expectations of those classes.