- Location
- Parkville Campus, United States of America
- Type
- Part-time
- Department
- Engineering
- Education
- PhD
- Source
- Workday
Description
Appointments are made on a term-by-term basis. Adjunct faculty are required to successfully complete an online Canvas LMS orientation course before being scheduled, as well as other training if required by the department. This is a face to face course. Local applicants only.
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Click on Park University’s Catalog for more information on programs and courses.
To ensure timely submission of your credentials, qualified applicants should apply following the specific instructions noted in each job posting. For all positions, a Masters Degree in the relevant field is required, along with related work and/or academic experience. For most postings, a Ph.D. or other terminal degree is preferred.
Park University is an Equal Opportunity Employer encouraging applications from women and minorities. The university will recruit and employ qualified personnel and will provide equal opportunities during employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability or status as a qualified protected veteran. Click here to view Park University’s EEOC and related policies.
Please Note: Park University participates in E-Verify for all positions at all campus center locations nationwide.
Minimum Qualifications
Master’s Degree in Business Analytics, Data Science, Statistics, Operations Management, Computer Science, or related fields with 18 credits in quantitative or classes in Analytics. Proficiency in the following areas of Business Analytics, Data Analytics, Predictive Analytics, and related fields. Knowledge of Python or R, and Tableau, and statistical software preferred. Ph.D. and a minimal of 5 years teaching experience or post-secondary teaching experience is preferred. Resume must be submitted in ACBSP format.
Course Description
This course will teach students to identify data sources and datasets relevant to business problems, gather, and manipulate data. Students will learn how to build and use relational, and non-relational databases as well as how to work with standalone datasets, APIs and other formats of data. This course will also focus on the main data manipulation methods such as querying, merging, filtering, and aggregation using various tools and their relation to the analytical solutions to business problems.
