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 course.

Prerequisites Skills Rationale
BS Degree or Equivalent Quantitative methods/analytics training and coding skills The DSPA graduate-level course requires a minimum level of quantitative skills
Quantitative Training Undergraduate calculus, linear algebra and introduction to probability and statistics These represent entry level skills required for the DSP course
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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