Montgomery College 2024-2025 Catalog 
    
    Jun 22, 2024  
Montgomery College 2024-2025 Catalog

Data Science AS: 416


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: 416

The degree provides an excellent opportunity for students wanting to increase their data literacy, improve their marketability, and/or prepare for a career in a data science field. It is also suitable for those who wish to advance their professional careers by supplementing their work experience or an existing college or graduate degree with data science knowledge. Students will use mathematics, statistics, and data science skills to tackle unstructured data, solve multifaceted problems, consider ethical implications, and make data-driven recommendations. Through hands-on experiences using a variety of the most ubiquitous data tools and technology, students will learn to build the skills necessary to explore, analyze, visualize, and communicate about large data sets. Additionally, students will explore ethical implications of the use of data in the data lifecycle. This program can be completed either on campus or online.

General Education Course Selections


Click here to view the Foundation/Distribution Courses  for selection to fulfill the General Education course requirements.

Suggested Course Sequence


Students should complete the required English and Math foundation courses within the first 24 credit hours. A suggested course sequence for full-time students follows. All students should review the Program Advising Guide and consult an advisor.

Second Semester


Third Semester


Fourth Semester


Total Credit Hours: 60


ENGL 101 /ENGL 101A , if needed for ENGL 102 /ENGL 103  or program elective. 

** Students are strongly encouraged to take two consecutive lab sciences courses. Examples include CHEM 131 /CHEM 132 PSCI 101 /PSCI 102 PHYS 203 /PHYS 204 

‡ MATH 284  may be substituted for MATH 264 .

† Program Electives: any MATH or CMSC course or GEOG 240  or GEOG 260 . Department strongly recommends CMSC 206 , CMSC 246 , and GEOG 240 CMSC 206  and CMSC 246  provide programming skills in Python and SQL, and GEOG 240  provides foundational knowledge of Geographic Information Systems (GIS). Not all program elective options transfer to all institutions. Please consult a data science program advisor or the transfer institution before selecting program elective courses.

AA and AS programs require one global and cultural perspectives (GCP) General Education course.

This program can be completed either on campus or online.

Program Outline / Degree Requirements


General Education Requirements


Foundation Courses


Distribution Courses


General Education Electives


Program Requirements


Total Credit Hours: 60


ENGL 101 /ENGL 101A , if needed for ENGL 102 /ENGL 103  or program elective.

** Students are strongly encouraged to take two consecutive lab sciences courses. Examples include CHEM 131 /CHEM 132 PSCI 101 /PSCI 102 PHYS 203 /PHYS 204 .

‡ MATH 284  may be substituted for MATH 264 .

† Program Electives: any MATH or CMSC course or GEOG 240  or GEOG 260 . Department strongly recommends CMSC 206 , CMSC 246 , and GEOG 240 CMSC 206  and CMSC 246  provide programming skills in Python and SQL, and GEOG 240  provides foundational knowledge of Geographic Information Systems (GIS). Not all program elective options transfer to all institutions. Please consult a data science program advisor or the transfer institution before selecting program elective courses.

AA and AS programs require one global and cultural perspectives (GCP) General Education course.

This program can be completed either on campus or online.

Program Outcomes


Upon completion of this program, a student will be able to:

  • Assess different analysis and data management techniques and justify the selection of a particular model or technique for a given task. 
  • Execute analyses of large and disparate datasets and construct models necessary for these analyses. 
  • Demonstrate competency with programming languages and environments for data analysis. 
  • Summarize and communicate findings of complex analyses in a concise way for a target audience using both graphics and statistical measures. 
  • Explain, evaluate, and apply ethical principles and practices in the data lifecycle.

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