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Data Science (Minor)

The Data Science minor consists of seven courses (21 credit hours), blending foundational and applied courses. Students from any discipline with a background in basic calculus and programming are able to enroll. 
The curriculum is designed to ensure a balance of theoretical knowledge and practical application. 
The Minor in Data Science is open to all students except those majoring in Data Science or Business Intelligence and Data Analytics (BIDA) program. To complete the minor the student must achieve at least a 2.0 grade-point average in seven three-credit courses from the following list, one three-credit course from each category. 
The lists will be updated by the minor advisory committee as needed.

For more information, view the course catalogue for current program requirements, course numbers and credit hours

Course Catalogue

Curriculum

Calculus (select one from the following):

  • Calculus 1 (Course Equivalents: MA 125)
  • Basic Calculus (Prereq. MA180 or MA120

Linear Algebra (select one from the following):

  • MA239 Linear Algebra for Data Science (not open to students who have taken or are taking MA 339)
  • Applied Linear Algebra (Prereq. MA132; MA230/231 recommended but not required)
  • Elementary Differential Equations (Prereq.MA132)

Probability and Statistics (select one from the following): 

  • General Statistics
  • Biostatistics (Spring Term)
  • Probability and Statistics (Prereq. MA132)
  • Probability and Statistics with Multivariate Analysis (Prereq. MA230 or MA231. Students may not enroll in STAT389 if they have credit for STAT383) (Fall Term)
  • Bioinformatics (Prereq. BY160 and BY214)
  • Probability & Statistics for Analytics

Introductory Programming (select one from the following):

  • Introduction to Computer Science I
  • (cross-listed HP103) Introduction to Engineering Use of the Computer
  • Intro to Business Intelligence and Data Analytics
  • Intro to Application Development (Spring Term)

Introduction to Data Science (select one from the following):

  • Introduction to Data Science (Coreq. STAT282, or STAT383, or STAT318, or STAT389) (Fall Term)
  • Applied Data Analytics (Prereq. IS110. Students may not receive credit for IS200 as well as IS301, offered Fall and Spring)
  • Information Visualization (Spring Term)

Data Management (select one from the following):

  • Database Design & Management
  • Database Systems (Prereq. Programming experience in a high-level language) (Spring Term)
  • Database Modeling, Design & Implementation

Select one course from the following: 

  • Algorithms and Data Structures (Prereq. CS142 or EE262 or EE363, and MA132) (Spring Term)
  • Computational Learning (Prereq.  CS344 and CS345, or consent of the instructor)
  • Artificial Intelligence (Prereq. CS344, CS250 and CS341 recommended)
  • Deep Learning (Prereq. CS142, EE262, or EE361, and MA339)
  • Computer Vision (Prereq. CS142 or EE262, and MA339)
  • Machine Learning on Biomedical Signals (Prereq. MA132, EE321, and BR400 or instructor approval) (Odd Fall Term)
  • Applied Machine Learning (Prereq.  IS237 or CS141 or EE261) (Fall Term)
  • Data Warehousing for Analytics (Prereq. IS314)
  • Data Science Tools (Prereq. M232, given when needed)
  • Bayesian Data Analysis (Prereq. STAT383 or MA/STAT381, or by instructor consent)
  • Time Series (given when needed)
  • A capstone/project-based course or application elective that the Minor Advisory Committee approves is also acceptable.
  • Any of the following grad-level courses:
    • Applications in Geospatial Analytics, Science, and Engineering (Spring Term)
    • Data Warehousing (Prerequisite: IA 510) (Fall Term)
    • Introduction to Big Data Architecture and Applications (Prereq. IA503, IA510, and IA626 or equivalents) (Summer Term)
    • Data Mining (Prereq. IA530 or equivalent) (Summer Term)
    • Applied Machine Learning (Prereq. IA530) (Spring Term)
       
  • Note: Prerequisite courses are listed below for reference purposes:
    • Introduction to Programming
    • Database Modeling, Design & Implementation
    • Optimization Methods for Analytics
    • Big Data Processing and Cloud Services

Suggested Sample Programs Based on Majors

Below is a sample pathway to earn a Data Science Minor tailored to various majors. Courses in bold indicate additional requirements beyond the core courses of the student's major.

  • Calculus I (Course Equivalents: MA 125)
  • Applied Linear Algebra (Prereq. MA132; MA230/231 recommended but not required)
  • Probability and Statistics (Prereq. MA132)
  • Introduction to Computer Science I
  • Introduction to Data Science (Coreq. STAT282, or STAT383, or STAT318, or STAT389) (Fall Term)
  • Database Design & Management or Database Systems (Prereq. Programming experience in a high-level language) (Fall Term)
  • Bayesian Data Analysis or Time Series (given when needed)
  • MA131 Calculus I (Course Equivalents: MA 125)
  • MA339 Applied Linear Algebra (Prereq. MA132; MA230/231 recommended but not required)
  • STAT383 Probability and Statistics (Prereq. MA132)
  • CS141 Introduction to Computer Science I
  • MA/DS241 Introduction to Data Science (Coreq. STAT282, or STAT383, or STAT318, or STAT389) (Fall Term) or IS301 Applied Data Analytics (Prereq. IS110. Students may not receive credit for IS200 as well as IS301, offered Fall and Spring)
  • CS460 Database Systems (Prereq. Programming experience in a high-level language) (Spring Term)
  • CSxxx
  • Calculus I (Course Equivalents: MA 125)
  • Elementary Differential Equations (Prereq.MA132)
  • Probability and Statistics (Prereq. MA132)
  • Introduction to Engineering Use of the Computer
  • Introduction to Data Science (Coreq. STAT282, or STAT383, or STAT318, or STAT389) (Fall Term) or Applied Data Analytics (Prereq. IS110. Students may not receive credit for IS200 as well as IS301, offered Fall and Spring)
  • Database Design & Management
  • Data Science Tools or Machine Learning on Biomedical Signals
  • Calculus I (Course Equivalents: MA 125)
  • Elementary Differential Equations (Prereq.MA132)
  • Probability and Statistics (Prereq. MA132)
  • Introduction to Engineering Use of the Computer
  • Introduction to Data Science (Coreq. STAT282, or STAT383, or STAT318, or STAT389) (Fall Term) or IS301 Applied Data Analytics (Prereq. IS110. Students may not receive credit for IS200 as well as IS301, offered Fall and Spring)
  • Database Design & Management or Database Systems (Prereq. Programming experience in a high-level language) (Fall Term)
  • Machine Learning on Biomedical Signals
  • Calculus I (Course Equivalents: MA 125)
  • Elementary Differential Equations (Prereq.MA132)
  • Probability and Statistics (Prereq. MA132)
  • Intro to Business Intelligence and Data Analytics or Intro to Application Development (Spring Term) or Introduction to Engineering Use of the Computer
  • Introduction to Data Science (Coreq. STAT282, or STAT383, or STAT318, or STAT389) (Fall Term)
  • Database Design & Management
  • Calculus I (Course Equivalents: MA 125)
  • Elementary Differential Equations (Prereq.MA132)
  • Probability and Statistics (Prereq. MA132)
  • Intro to Business Intelligence and Data Analytics
  • Introduction to Data Science (Coreq. STAT282, or STAT383, or STAT318, or STAT389) (Fall Term) or IS301 Applied Data Analytics (Prereq. IS110. Students may not receive credit for IS200 as well as IS301, offered Fall and Spring)
  • Database Design & Management
  • Basic Calculus (Prereq. MA180 or MA120)
  • Elementary Linear Algebra (Prereq. MA 131 or MA181, Not open to Mathematics or Applied Math and Stats majors; not open to students who have taken or are taking MA 232 or MA 339)
  • General Statistics or Biostatistics (Spring Term) or Bioinformatics (Prereq. BY160 and BY214)
  • Intro to Business Intelligence and Data Analytics or Intro to Application Development (Spring Term)
  • Applied Data Analytics (Prereq. IS110. Students may not receive credit for IS200 as well as IS301, offered Fall and Spring)
  • Database Design & Management
  • Applied Machine Learning (Prereq.  IS237 or CS141 or EE261) (Fall Term)

Please Note:

  • Students seeking Math Minor should take MA/DS241 instead of IS301.
  • Students majoring in engineering are recommended to take ME or EE as their professional elective course.
  • Students with good programming background are encouraged to take CS as their professional elective course.
  • Students with good information systems background are recommended to take a grad-level IA course as their professional elective course.
  • Students with good statistics background are recommended to take STAT385 or STAT386 as their professional elective course.