Applied Data Science

Students must fulfill the Oakland University General Education Requirements, the General College of Arts and Sciences Requirements, the College of Arts & Sciences Exploratory Requirement, and the Major Requirements. Additionally, students must complete a sufficient number of free elective courses to meet the overall credit requirement for their degree. A minimum of 120 credits is required, although some degree programs may require a higher total credit count.

General Education Requirements

In order to graduate on-schedule without taking additional courses, it is highly recommended that students meet with an Undergraduate Academic Adviser concerning the selection of all of their general education courses.

Each candidate for an Oakland University baccalaureate will need to satisfactorily complete approved courses in each of the following areas: Foundation, Exploration, Integration, Writing, U.S. Diversity and Capstone. For details, refer to the General Education Requirements section of the catalog.

Requirements for the Major in Applied Data Science, B.S.

To earn a Bachelor of Science degree with a major in Applied Data Science, students must complete 120 credits including:

Required Courses (52 credits)

• course Calculus I (4)

• course Calculus II (4)

• course Linear Algebra (4)

• course Discrete Mathematics (4)

• course Applied Probability and Statistics (4)

• course Applied Linear Models I (4)
  or course Multivariable Calculus (4) 

• course Statistical Computing (4)

• course Statistical Methods in Data Science (4)

• course Introduction to R for Data Science (4)

• course Introduction to Python Programming and Unix (4)

• course Object-Oriented Computing (4)

• course Data Structures (4)

• course Database Design and Implementation (4)

Applied Quantitative Electives (16 credits)

• course Introduction to Differential Equations with Matrix Algebra (4)

• course Applied Matrix Theory (4)

• course Design and Analysis of Algorithms (4)

• course Numerical Methods (4)

• course Applied Numerical Methods: Matrix Methods (4)

• course Technical Analysis of Stocks Trading Data (4)

• course Mathematical Models in Biology (4)

• course Applied Mathematics: Discrete Methods I (4)

• course Applied Linear Models II (4)

• course SAS Programming with Statistics Applications (4)

• course Introduction to Mathematical Statistics I (4)

• course Introduction to Mathematical Statistics II (4)

• course Multivariate Statistical Methods (4)

• course Discrete Data Analysis (4)

• course Stochastic Processes I (4)

• course Nonparametric Methods (4)

• course Statistical Methods in Sample Surveys (4)

• course Time Series I (4)

• course Bayesian Data Analysis (4)

• course Reliability and Life Data Analysis (4)

• course Engineering Operations Research (3)

• course Linear and Integer Optimization (4)

• course Nonlinear Optimizations (4)

• *Other courses, if approved by the program coordinator.

Application Area Electives (minimum 14 credits)

Geoinformatics:

• course Introduction to Geographic Information Systems (4)

• course Remote Sensing (RS) Using Aerial and Satellite Imagery (4)

• course Spatial Data Modeling and Analysis (4)

• course Geographic Information System Analysis for Sustainability (4)

Healthcare:

• course Biology I (4)

• course Genetics (4)

• course Functional Genomics and Bioinformatics (4)

• course Introduction to Health and Health Behaviors (3)

• course Organizational Behavior and Health Care Systems (3)

• course Introduction to Health Literacy (4)

• course Public Health Program Implementation (4)

• course Population Health, Health Policy, and Healthcare Delivery (4)

Computer Science:

• course Introduction to Data Science in Python (4)

• course Data Visualization (3)

• course Artificial Intelligence (4)

• course Machine Learning (4)

• course Information Retrieval and Knowledge Discovery (4)

• course Big Data Analysis with Cloud Computing (4)

Business:

• course Introduction to the Global Economy (4)

• course Financial Markets and Economy (3)

• course Marketing (3)

• course Digital Marketing (3)

• course Business Database Systems (3)

• course Business Analytics (3)

• course Practical Computing for Data Analytics (3)

• course Economics in Today’s World (4)

• course Applied Time Series Analysis in Business (3)

• course Using “BIG” Data for Economic Problems (3)

• course Econometrics (3)

• course Digital Marketing Design and Analysis (3)

• course Data Analytics for Marketing and Business Strategy (3)

Engineering:

• course Introduction to Industrial and Systems Engineering (4)

• course Engineering Statistics and Economic Analysis (4)

• course Engineering Operations Research (3)

• course Data Analytics (4)

• course Engineering Risk Analysis (4)

Mixed:

• Mix of courses from at least 2 of the above tracks.

Other courses, if approved by the Program Coordinator.

Other requirements: General Education, other prerequisite courses (the rest of credit hours).  

Applicable Minors

All Minors are applicable to this major with the exception of the following Minor(s): Applied Mathematics, Child Welfare, International Orientation for EGR/CS, and Sustainability Engineering.