Overview
In today’s data-driven society, making informed decisions that affect business growth and outcomes is becoming powered by data science. The Analytics program 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 pursue a baccalaureate through the UNH Manchester Pathway 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 Trade Port, 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.
Career Options
Graduates of the Analytics program have the option to work in industry or the government.
Related Degrees
Transfer Opportunities
There is a Transfer Pathway to UNH Manchester’s Analytics and Data Science B.S. program.
Curriculum Outline
The classes and coursework required is as follows:
First Year: Fall Semester
Course ID | Course | Theory | Lab | Credits |
---|---|---|---|---|
FYE101G | First Year Seminar | 1 | 0 | 1 |
CIS112G | Introduction to Object Oriented Programming | 3 | 0 | 3 |
MATH210G | Pre-Calculus | 4 | 0 | 4 |
ENGL110G / 111G | College Composition I / College Comp I with Lab | 4 | 0/2 | 4/5 |
Lab Science Elective* | 4 | |||
Total Credits | 16-17 |
First Year: Spring Semester
Course ID | Course | Theory | Lab | Credits |
---|---|---|---|---|
CIS148G | Introduction to Java Programming | 2 | 2 | 3 |
BUS110G | Introduction to Business | 3 | 0 | 3 |
Foreign Language / Humanities / Fine Arts Elective (ARTS125G Preferred)** | 3 | 0 | 3 | |
MATH230G | Calculus I | 4 | 0 | 4 |
MATH235G*** | Statistics for Engineers and Scientists | 4 | 0 | 4 |
Total Credits | 17 |
* Lab Sciences: BIOL106G, 150G, 108G, 109G, 110G, 120G, 160G, 210G, BTEC105G, CHEM110G, 115G, 116G, PHYS135G, 136G, 290G, 295G
** Must choose from ARTS123G, 105G, 117G, 125G, 127G, or 137G
*** IF MATH150G/152G is needed, students will need to take the course in the summer prior to year one in order to be on track; these students should take MATH235G in spring of year two.
^Theory, lab, and credit hours will vary depending on the elective course chosen.
Summer Semester Prior to Year One (if needed)
Course ID | Course | Theory | Lab | Credits |
---|---|---|---|---|
MATH150G | College Algebra | 4 | 0 | 4 |
Total Credits | 4 |
Second Year: Fall Semester
Course ID | Course | Theory | Lab | Credits |
---|---|---|---|---|
DATA210G | Elements of Data Science | 3 | 0 | 3 |
CIS113G | Database Design and Management | 2 | 2 | 3 |
MATH245G | Introduction to Linear Algebra | 4 | 0 | 4 |
ENGL215G | Writing Technical Documents | 3 | 0 | 3 |
SOCI120G | Society and Technological Change | 3 | 0 | 3 |
Total Credits | 16 |
Second Year: Spring Semester
Course ID | Course | Theory | Lab | Credits |
---|---|---|---|---|
DATA220G | Introduction to Data Analysis with R | 3 | 0 | 3 |
CIS210G | Data Structures with Elementary Algorithms | 3 | 2 | 4 |
MATH250G MATH235G* | or | 4 4 | 0 0 | 4 4 |
CIS177G | Introduction to Python | 2 | 2 | 3 |
Total Credits | 14 |
Summer Semester
Course ID | Course | Theory | Lab | Credits |
---|---|---|---|---|
DATA225G | Analytics Capstone | 2 | 0 | 2 |
Total Credits | 2 |
Total Overall Credits: 65-66
* Students who started with MATH150G/152G will take MATH235G in 2nd year instead of MATH250G Calculus II; Total MATH credits: 20
Program Outcomes
Select a topic of research for which sufficient data exist or data can be simulated in order to answer a question involving statistical analysis, and create a reproducible research report that incorporates and illustrates competent knowledge with the following:
• Use advanced R packages and constructs and create R functions
• Develop reproducible analysis report using Markdown and generated in 3 formats: html, Word doc and pdf doc
• Apply the Cross-Industry Standard Process for Data Mining (CRISP–DM) methodology to the analysis project
• Perform linear regression and multiple linear regression on real-world data sets that are applicable to the project
• Apply statistical methods such as clustering, classification, time series analysis and/or factor analysis as applicable to the project selected and communicate results of these analyses
• Develop advanced visualizations in support of communicating results of statistical analysis as part of the final report in an aesthetically appropriate manner