In today’s data-driven society, making informed decisions that affect business growth and outcomes is becoming powered by data science. The Practical Data Science Certificate at GBCC is designed for those who want to understand how to work with raw data to process, analyze, and make conclusions using predictive modeling.  Students have the opportunity to further the AS in Analytics or move directly into the field.

Why Great Bay?

We have an 11 to 1 student to faculty ratio which provides students a robust opportunity for greater campus involvement and success.  With an excellent proximity to neighboring industries at the Pease Tradeport, the University of New Hampshire, and the Naval Shipyard, Great Bay Community College’s students have the flexibility to enhance their education to suit their professional goals.

Related Degrees

Fall Semester

Course IDCourseTheoryLabCredits
ARTS125GVisual Language303
DATA210GElements of Data Science303


(MATH225G)Probability and Statistics(4)(0)(4)
Total Credits10

Spring Semester

Course IDCourseTheoryLabCredits
CIS177GIntroduction to Python223
MATH235GStatistics for Engineers and Scientists404
DATA220GData Analysis with R303
Total Credits10

Summer Capstone

Course IDCourseTheoryLabCredits
DATA225GAnalytics Capstone202
Total Credits2

Total Overall Credits : 22

Note: The Practical Data Science Certificate is a rigorous program. Students are expected to spend additional time beyond the minimum to complete requirements and achieve success. Students are also expected to have college level reading, writing and math skills as soon as possible after declaring this major.

Program Outcomes 

  • Write and organize analysis scripts that utilize the functional programming nature of a statistical programming language and vectorization model
  • Work with all modern data formats, including XML, CSV, JSON, XLS (Excel), XHTML (web pages), and understand how to appropriately transform this data for use in structured analysis projects and reporting
  • Visualize data for use in exploratory data analysis as a pre-cursor to statistical analysis of data sets; effectively communicate preliminary results toward further understanding of the problem and solution
  • Apply the Cross-Industry Standard Process for Data Mining (CRISP–DM) methodology to any analysis project; develop reproducible analysis reports generated in a variety of formats
  • Understand the concepts of modern statistical methods and analyses and how they apply in data analysis projects and especially how they are used in more advanced predictive modeling
  • Develop advanced visualizations in support of communicating results of statistical analyses; produce clear, concise reports in conclusion of analysis of a topic as an effective demonstration of the data as it serves to enlighten and inform

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