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Decision analytics encompasses the systematic process of inspecting, cleaning, transforming, and modeling data to uncover valuable insights, draw conclusions, and facilitate effective decision-making. This course serves as an introduction to fundamental statistical and machine learning techniques, equipping students with managerial perspectives on leveraging both small and big data for problem identification, analysis, and decision-making. The curriculum covers a range of topics, including data analysis techniques, the utilization of computing tools for applying these methods, and effectively communicating findings to non-experts through numerical and graphical data presentations. Students are required to use Microsoft Excel and IBM SPSS (Statistical Package for the Social Science which will be taught in the class). The teaching approach will encompass case studies and the Socratic method to illustrate each concept effectively. By the end of this course, students will have gained essential skills in data analysis, machine learning, and decision-making, empowering them to make informed business decisions and communicate their findings in clear and concise reports.