Data analytics is having an enormous impact on society and the economy. As the field of data analytics evolves, there will be an increasing need for technicians with skills in data analytics who can work in a wide range of job roles. Unfortunately, the number of students pursuing careers in data analytics is not keeping pace with workforce demand, particularly among populations underrepresented in science, technology, engineering, and mathematics.
This project aims to prepare the future workforce by establishing pathways that generate interest in data analytics careers, support the transfer of data literate high school students into postsecondary programs, and prepare them to fill the growing demand for data analytics technicians. Project activities will provide multiple opportunities for high school students to explore and begin a data analytics pathway.
The project will leverage several strategies for attracting and retaining populations typically underrepresented in STEM careers and support student success. These strategies include equitable and inclusive instructional design, experiential learning, accelerated credential completion, affordability, and comprehensive student support.
In this project, Sinclair Community College will partner with local industry in southwest Ohio to develop a data analytics technician pathway for students that will facilitate their transfer into college and support their postsecondary credential completion in preparation for careers in data analytics. A stackable certificate model will be developed to prepare students for entry-level data analytics jobs and be beneficial to those who need to earn a credential quickly to enter the job market. The model will also provide clear pathways to further education that can lead to higher-paying jobs.
The project will share data literacy concepts and information about data science careers with high school students and teachers. Dual high school/college credit courses will be offered to accelerate college credential completion while students are still in high school. The project will engage high school teachers in data literacy professional development opportunities to increase learning in statistics using R and Python for data analytics. College students will benefit from enhanced experiential learning opportunities designed to increase data career-related technical skills and connections with potential employers.
Sinclair Data Analytics Pipeline Grant 1 Pg Description NSF ATE 2022.pdf
MAT 1455 Introduction to Data Science
An introductory math course using R in data science for students interested in information technology, computer science, and related fields. Topics include curation of data; enhanced data visualization; statistical models, estimation, and prediction; and applications of data science.
CIS 1160 Introduction to Data Literacy
In this course, students will learn how to identify data sources and evaluate whether data is credible and relevant. The course will introduce techniques to cleanse, analyze, and manage data. Visualization tools are covered in the course to assist in identifying and communicating data patterns and trends. Presentation of data findings and communicating meaning through storytelling is an important element of this course. In addition, students will gain an understanding on the impact of data in our society.
CIS 2267 Advanced Python for Data Analytics
Students will learn how to obtain, cleanse, and prepare data, use supervised methods to predict and categorize data, and present their findings. This course will include analyzing data using Pythons tools perform cluster analysis and PCA, analyzing data using Pythons tools to classify and predict outcomes, present the findings, identifing data sources including big data and cleanse the data using Python tools.
This material is based upon work supported by the National Science Foundation under Grant DUE 2055411. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.