Purpose: This research aims to demonstrate the application of Lean Six Sigma methodologies in redesigning a graduate program in Data Analytics. The goal was to align the program’s curriculum with the dynamic demands of the data-driven industry while maintaining its reputation for producing highly skilled professionals. Study Design/Methodology/Approach: The study employed a Lean Six Sigma framework to guide the redesign process. This research involved a comprehensive analysis of the existing curriculum, including student feedback and collaboration with external employers to identify industry-relevant skills. Market research and job skills analysis, as well as informed curriculum adjustments. The restructuring involved transitioning from ten courses to six core courses and four electives, focusing on hands-on experience and flexibility. Assignments and projects were redesigned to incorporate real-world data and scenarios, while peer reviews were introduced to foster collaborative learning. Findings: The findings indicate that the Lean Six Sigma approach facilitated the development of a more streamlined and industry-responsive curriculum. Core courses were redesigned to cover fundamental skills such as statistical analysis, data wrangling, visualization, and programming using various tools. The introduction of peer reviews and stacked credentials enhanced the learning experience and graduates’ marketability. Students were empowered to select datasets aligned with their career interests, build online portfolios, and customize their educational journey. Originality/Value: This research contributes to the field by showcasing a structured application of Lean Six Sigma principles in higher education curriculum redesign. By integrating feedback from students, collaboration with industry partners, and market research insights, the program was able to evolve to meet the evolving demands of the data analytics field. The emphasis on hands-on experience, flexibility, and industry relevance ensures that graduates are well-prepared to succeed in the data-driven job market, adding value to both students and employers.