Data Analytics Technician Advancement
Building an Academic Pathway for Data Analytics Technician Advancement (DATA)
Columbus State Community College, in partnership with Central Ohio companies, regional high schools, and two regional universities will develop a new career pathway in Data Analytics. The career pathway will have two tracks: one for incoming students from regional high schools; and one for veterans and underemployed incumbent workers.
In autumn 2015, Columbus State Computer Science faculty members hosted a series of focus group sessions with an Industry Leadership Team of IT industry leaders. The results indicated the following gaps in central Ohio:
- Absence of two-year and four-year data analytics educational programs in the region.
- Lack of awareness of education in data analytics for high school students in the region.
- Lack of virtual delivery for data analytics development programs.
The few existing programs are traditional, classroom-based, and there is a need for collaborative, online education. Research conducted by project partner Educational Development Center corroborate these findings. This project will address these gaps by creating a data analytics technician 2+2+2 career pathway.
This site is intended for project information dissemination on the DATA grant funded by National Science Foundation and their Advanced Technological Education (ATE) program. For more information on this grant project, please see our project flyer.
- Finalize and expand an Industry Leadership Team so that businesses can co-lead the project work and provide maximum benefit to students.
- Enhance existing curricular content and create new courses within the Associate of Applied Science degree in Computer Science, Information Systems and Analytics track at Columbus State Community College.
- Develop an internship guide for data analytics students in collaboration with Education Development Center’s Advanced Technological Education project Creating Pathways for Big Data Centers.
- Expand the knowledge base in data analytics for non-computer science majors through a learning module in analytics to provide foundational content that can be contextualized.
- Establish a model articulation agreement in which regional four-year universities can serve as the final link in a 2+2+2 training pipeline.
- New industry-led curriculum in data analytics including three new courses resulting in a new DATA certificate that is stackable toward an Associate’s Degree in Information Systems & Analytics. The new curriculum will include digitized content and experiential learning through a capstone course.
- A 2+2+2 Data Analytics Technician Advancement (DATA) Pathway to coordinate and facilitate the education pipeline from high school to the community college. It will include a template articulation agreement with regional universities.
- A collaboratively developed internship guide for data analytics technicians working with the EDC.
- Learning modules that educate students on the foundational aspects of data analytics. These will be adaptable for contextualized courses within other non-computer science programs.
- Outreach plan for veteran populations to attract active and former military into the DATA program.
What is ATE?
The Data Analytics Technician Advancement (DATA) project is being funded by a National Science Foundation Advanced Technological Education (ATE) Grant (DUE 1700454). With an emphasis on two-year colleges, Advanced Technological Education program focuses on the education of technicians for the high-technology fields that drive our nation's economy. The program involves partnerships between academic institutions and industry to promote improvement in the education of science and engineering technicians at the undergraduate and secondary school levels. The ATE program supports curriculum development; professional development of college faculty and secondary school teachers; career pathways to two-year colleges from secondary schools and from two-year colleges to four-year institutions; and other activities.
This material is based upon work supported by the National Science Foundation under Grant No. 1700454.