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Managing Analytic Projects

94-881

Units: 6

Description

With the growing demand for data science and AI skills, there are many options for students to learn fundamentals of data and analytics modeling. There are fewer opportunities to learn how to manage analytics projects, which often involve leading teams with diverse skills and interacting with stakeholders in a variety of roles. Using a decision-driven framework, this course offers students practical guidance and experience around the process of initiating, delivering, and evaluating analytics projects. It will draw on experience from a consulting perspective, talking about analytics with clients and delivering analytics related engagements.
The course will cover the following topics:
● Starting the analytics conversation: Identifying needs, understanding constraints
● Planning and executing analytics projects: Sizing, staffing, communication
● Making choices around data, analytics, and visualizations: Sourcing, techniques, technologies
● Analytics in the enterprise: Communications, organizing talent, strategy

Students cannot take this course as pass/no pass, or audit this class.

Learning Outcomes

  • Recognize analytics opportunities and converse with stakeholders to elicit project requirements
  • Identify data sources, analytics and visualization techniques relevant to an analytics problem
  • Create and evaluate analytics project plans
  • Anticipate and address common challenges in analytics projects

Prerequisites Description

Students should have completed a statistics course. Students may wish to review the fundamentals of statistics and probability in the free online learning class at https://oli.cmu.edu/courses/probabilitystatistics-open-free/ . Proficiency with at least one analysis environment (e.g. Excel, Python, R, or SAS) required. Experience with advanced analytics (data science, artificial intelligence) highly desirable.

Syllabus