Although there are expected variations in student backgrounds, interests, motivations, expectations, and learning styles, the below prerequisites serve as a guideline of the foundational knowledge and experience that will be helpful for the successful completion of the Data Science and Predictive Analytics MOOC.

Prerequisites Skills Rationale
BS Degree or Equivalent Quantitative training and coding skills as described below The DS certificate is a graduate program requiring a minimum level of quantitative skill
Quantitative Training Undergraduate calculus, linear algebra and introduction to probability and statistics These are the entry level skills required for most upper-level undergraduate and graduate courses in the program
Coding Experience Exposure to software development or programming on the job or in the classroom Most DS practitioners need substantial experience with Java, C/C++, HTML5, Python, PHP, SQL/DB
Motivation Significant interest and motivation to pursue quantitative data analytic applications Dedication for prolonged and sustained immersion into hands-on and methodological research

Students should have prior experience with college level (undergrad) mathematical modeling, statistical analysis, or programming courses or permission of the instructor. Some MOOCs may be taken as prerequisites, e.g., Corsera, EdX1, EdX2. Additional examples of remediation courses are provided in the DSPA self-assessment (pretest).

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