The learners in this experience are completing their final semester in a Masters of Science in Data Science and Analytics. Their technical skills include: Data Acquisition, Wrangling, and Cleansing Statistisical Analysis including Supervised and Unsupervised models such as regression, monte carlo, and cluster analysis. Machine Learning Python & R Programming SQL Database and NoSQL They are capable of completing a project - from scratch - that includes processing and cleansing data, identifying and applying the appropriate analysis techniques, and delivering and communicating resutls.

Looking to elevate your organization, and bring it to the next level? Bring on learners from Clemson University to be your learner-consultants, in a project-based experience. Learners will work on one main project over the experience of the semester, connecting with you as needed with virtual communication tools. Learners in this program/experience will learn and apply various decision modeling tools, such as Queueing Theory, Bayes decision analysis, network optimization, make/buy analysis, breakeven analysis, assignment, resource allocation with the utilization of linear programming and/or simulations. They will also have extensive descriptive and predictive analytic capabiities.

These learners are MBA students who will have completed 12 weeks of a 16-week course on data visualization. They will have knowledge and skills including: Intermediate-level Tableau skill. Intermediate-level understanding of data visualization best practices. Beginner-level data cleansing skills. Basic understanding of human visual and cognitive processing. Intermediate-level skill in data storytelling.

Learners are second-year MBA students. Most are working professionals. This is a cohort-based, executive-style MBA program in Analytics. The best match projects for these students will be projects that combine aspects of descriptive and predictive analytics. Students are required to deliver at least part of the solution using Tableau. Students will be focusing on: Describing and explaining various systems development lifecycles and methodologies (as an overview) Understanding the overall systems implementation and operation issues, including technical, managerial, and interpersonal aspects of implementing enterprise systems in organizations. Developing a deep understanding of how technical and organizational factors work together to ensure the success of analytics system implementation. Using Tableau to tell detailed data stories. Organizing in a team environment to share requirements, code, and other resources to deliver a real-world analytics project. Project activities that learners can complete may include, but are not limited to:  Data scrubbing and preparation Data Visualization using Tableau Data Analysis using Excel, Tableau, and Python Identifying hidden relationships in historical data. Identifying particular opportunities for action.