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Analytics

Associate in Science
Analytics
Home > Programs > Analytics
Overview
Curriculum
Outcomes
Faculty
Overview

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

  • Engineering A.S.
  • Bioengineering A.S.
  • Chemistry A.S.

Transfer Opportunities

There is a Transfer Pathway to UNH Manchester’s Analytics and Data Science B.S. program. 

Curriculum

Curriculum Outline

The classes and coursework required is as follows:

First Year:  Fall Semester

Course IDCourseTheoryLabCredits
FYE101GFirst Year Seminar101
CIS112GIntroduction to Object Oriented Programming303
MATH210GPre-Calculus404
ENGL110G / 111GCollege Composition I / College Comp I with Lab40/24/5
 Lab Science Elective*  4
Total Credits16-17

First Year: Spring Semester

Course IDCourseTheoryLabCredits
CIS148GIntroduction to Java Programming223
BUS110GIntroduction to Business303
 

Foreign Language / Humanities / Fine Arts Elective

(ARTS125G Preferred)

303
MATH230GCalculus I404
MATH235G***Statistics for Engineers and Scientists404
Total Credits17

* Lab Sciences: BIOL106G, 150G, 108G, 109G, 110G, 120G, 160G, 210G, BTEC105G, CHEM110G, 115G, 116G, PHYS135G, 136G, 290G, 295G

*** 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 IDCourseTheoryLabCredits
MATH150G/MATH152G

College Algebra / College Algebra Plus or MATH170G

404
Total Credits4

Second Year: Fall Semester

Course IDCourseTheoryLabCredits
DATA210GElements of Data Science303
CIS113GDatabase Design and Management223
MATH245GIntroduction to Linear Algebra404
ENGL215GWriting Technical Documents303
SOCI120GSociety and Technological Change303
Total Credits16

Second Year: Spring Semester

Course IDCourseTheoryLabCredits
DATA220GIntroduction to Data Analysis with R303
CIS210GData Structures with Elementary Algorithms324

MATH250G 

MATH235G*

Calculus II

or

Statistics for Engineers and Scientists

4

4

0

0

4

4

CIS177GIntroduction to Python223
Total Credits14

Summer Semester

Course IDCourseTheoryLabCredits
DATA225GAnalytics Capstone202
Total Credits2
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

Outcomes

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

Faculty

  • John Mannarini

    John Mannarini

    Math
    Program Coordinator / Professor
    (603) 427-7714
    [email protected]

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