Managing Analytic Projects
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, visualizations and infrastructure: Sourcing, techniques, technologies, integration, security, pipelines
● Analytics in the enterprise: Communications, ethics, organizing talent, strategy
- 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, identifying and mitigating project risks
Students should have completed a statistics course. If they do not have statistics background the CMU online, self-paced, free class at https://oli.cmu.edu/courses/probability-statistics-open-free/ is recommended . 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.