SCO 6191: Big Data Analytics in Supply Chains

2 CreditsOnline Available

With the advancement of digital technologies and networking capabilities, firms are actively engaged in capturing “big” data related to their supply chains. Firms recognize the immense potential in mining big data for improving the quality and timeliness of decisions, and becoming proactive in sensing and responding to external and internal signals of threats and opportunities. The course develops the capability to analyze and interpret structured and unstructured data that is fundamental to managing supply. The data analytics methods covered in the course include statistical methods (e.g., multivariate regression, logistics regression, GLMM, LASSO), machine learning methods (e.g., support vector machine, ensemble methods – random forest, gradient boosting model) and optimization methods (e.g., deterministic and stochastic methods). Through a combination of operations analysis case studies and hands-on exercises, students learn (i) various facets of data analytics: data access, data aggregation, data analysis and data visualization; (ii) appropriateness and inappropriateness of big data analytic methods; and (iii) big data based predictive analytics. The final course project involves designing and testing of prototype systems in supply chain and operations settings of companies.

View on University Catalog

All Instructors

A- Average (3.798)Most Common: A (52%)

This total also includes data from semesters with unknown instructors.

205 students
WFDCBA
  • 3.72

    /5

    Recommend
  • 3.51

    /5

    Effort
  • 3.81

    /5

    Understanding
  • 3.88

    /5

    Interesting
  • 3.57

    /5

    Activities


      Contribute on our Github

      Gopher Grades is maintained by Social Coding with data from Summer 2017 to Fall 2023 provided by the Office of Institutional Data and Research

      Privacy Policy