SCUP

Data Analytics, Part-Time Instructor 001-PTF-48

Bellevue College

Posted: 6/17/20
Deadline: 9/9/20

About This Organization

Bellevue College is a student-centered, comprehensive and innovative college, committed to teaching excellence that advances the life-long educational development of its students while strengthening the economic, social and cultural life of its diverse community. The college promotes student success by providing high-quality, flexible, accessible educational programs and services; advancing pluralism, inclusion and global awareness; and acting as a catalyst and collaborator for a vibrant region.

Job Duties

Bellevue College maintains a pool of qualified applicants interested in temporary teaching positions on a part-time basis. Positions may become available at any time and are typically filled on a quarterly basis, with no expectation of continued employment beyond the current appointment.

The IBIT Division at Bellevue College is accepting applications for qualified, part-time instructors to teach 5 – 10 credits of Data Analytics courses. Classes are expected to occur during the evening and/or online.

This individual is responsible for teaching courses that may include, but not limited to Introduction to Analytics, Data Acquisition & Management, Multivariate Analytics, and Predictive Analytics.

Required Qualifications

• Master’s degree from a regionally accredited university in Applied Math, Applied Statistics, Computer Science, IT or a related field; or equivalent experience.
• Substantial, recent full-time experience in the industry utilizing DA technologies and/or teaching experience in Data Analytics.
• Demonstrate ability to work with diverse group
• Experience applying analytical methods to real-world problems, preferably with business application, using both big and small data constructs.
• Strong foundational knowledge (both theoretical and applied) of computational statistics and statistical/non-statistical modeling, including but not limited to generalized linear modeling, survival analysis, variable reduction techniques (PCA/SVD), machine learning, and time series modeling
• Broad exposure to the various topical areas constituting the field of data analytics, including but not limited to applied statistics, data mining, optimization, simulation, web analytics, marketing analytics, data visualization, and data management
• Proficiency with data analysis tools and platforms such as SAS, R, Python, and SQL