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Data Science

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Computer Science & EngineeringUG

General

Requirements

Major Dependent Concentration(s)

Program Requirements

The program in Data Science leading to a Bachelor of Science degree prepares students for a productive career in the industry and for graduate studies in diverse disciplines. The curriculum integrates quantitative analysis and computer science, setting it apart from other fields. Through hands-on projects and practical exploration of programming languages like Python and R, students delve into machine learning algorithms, data visualization techniques, and database management, gaining invaluable insights into complex dataset analysis. The curriculum places a strong emphasis on data ethics, ensuring graduates are adept at responsibly navigating diverse contexts. Capstone projects encourage the application of knowledge to solve complex problems, fostering critical thinking and innovation. With a strong focus on professional development, including technical communication and teamwork, the program ensures that graduates are well-prepared for a dynamic career in data science, equipping them with the skills and knowledge needed to excel in the rapidly evolving field.

Program Educational Objectives

In the course of their careers, graduates of the Data Science program will:

  • Work productively to design, implement, and improve solutions to data problems.

  • Remain current in their profession through lifelong learning, including graduate school.

  • Exhibit teamwork and leadership as well as exercise their profession with the highest level of ethics and social responsibility.

Requirements for the Bachelor of Science in Data Science

To earn a Bachelor of Science degree with a major in Data Science students must complete a minimum of 128 credits and meet the following requirements:

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: Foundations, Explorations, Integration, Writing Intensive, and US Diversity. For details, refer to the General Education Requirements section of the catalog. In order to satisfy both general education and other program requirements, in some of the General Education areas students should select from the courses listed below.

Foundations
• Writing Foundations (course)

• Formal Reasoning (Satisfied by course; see Quantitative Foundations)

Explorations: One course from each of the 7 areas
• Arts

• Language and Culture

• Global Perspective - course will satisfy the Global Perspective General Education requirement and act as a prerequisite for students pursuing the Economics Application Area.

• Literature

• Natural Science and Technology (Satisfied by an approved science elective) - course will satisfy the Natural Science and Technology General Education requirement and act as a prerequisite for students pursuing the Genomics Application Area.

• Social Science - course will satisfy the Social Science General Education requirement and act as a prerequisite for students pursuing the Economics or Risk Management Application Areas.

• Western Civilization (Satisfied by course; see Additional Major Requirements)

Integration
• Knowledge Applications (satisfied by course; see Quantitative Foundations)

U.S. Diversity
• May be met by an approved course in the Explorations area

Capstone and Writing Intensive
• Capstone (satisfied by course; see Required Professional Subjects)

• Writing Intensive in the Major (satisfied by course; see Required Professional Subjects)

• Writing Intensive in General Education (may be met by an approved course in the Explorations Area)

Additional Major Requirements
All Data Science students must complete the following requirement. The course also satisfies the Western Civilization General Education requirement.

• Professional Ethics: course Introduction to Ethics in Science and Engineering

Quantitative Foundations

  • course Calculus I (4)

  • course Calculus II (4)

  • course Discrete Structures with Applications (4)
    or course Discrete Mathematics (4) 

  • course Computational Linear Algebra (4)
    or course Linear Algebra (4) 

  • course Applied Probability and Statistics (4)

Approved Science Elective

Take 1 of the following:

  • course Biology I (4)

  • course Biology II (4)

  • course Biology and Society (4)

  • course Chemistry, Society Health (4)

  • course General Chemistry I (4) and course General Chemistry Laboratory I (1) 

  • course Introduction to Environmental Studies (4)

  • course Introduction to Health and Health Behaviors (3)

  • course Language and the Brain (4)

  • course Earth Science/Physical Geography (4)

  • course The Physics of Everyday Life (4)

  • course Introductory Physics I (4) and course General Physics Lab I (1) 

Data Science Core

  • course Introduction to Python Programming and Unix (4)

  • course Object-Oriented Computing (4)

  • course Data Structures (4)

  • course Introduction to Data Science in Python (4)

Required Professional Subjects

  • course Database Design and Implementation (4)

  • course Security and Privacy in Computing (4)

  • course Design and Analysis of Algorithms (4)

  • course Data Visualization (3)

  • course Contemporary Issues in Data Science (3)

  • course Information Retrieval and Knowledge Discovery (4)

  • course Big Data Analysis with Cloud Computing (4)

  • course Data Science Capstone (4)

  • course Applied Linear Models I (4)
    or course Statistical Methods in Data Science (4) 

  • course Introduction to R for Data Science (4)

Professional Electives

Students must complete 3 professional elective courses. At least 2 of them must be from Group A.  Any remaining course can be from either Group A or Group B.

Group A

  • course Software Engineering and Practice (4) 

  • course Artificial Intelligence (4) 

  • course Deep Learning and Applications (4) 

  • course Machine Learning (4) 

  • course Natural Language Processing (4) 

  • course Cloud Computing (4) 

  • course Database System I (4) 

  • course Bioinformatics (4) 

  • course Data Analytics (4) 

Group B

  • Any CSI or STA designated course numbered 3000 or higher

Application Area Courses

Students are required to take 2 courses from an area in which knowledge of Data Science can be applied. Each course in this area must be at least 3 credits. The application area courses should be completed before taking course (Data Science Capstone). Courses used to satisfy the application area requirement may also be used to meet the General Education Requirement. The application area courses need not to be from a single rubric or department but together they should provide a context for Data Science activities. A list of application area courses is provided below. If students are interested in selecting the application area courses from outside the provided list, they are advised to work with a faculty adviser from the Department of Computer Science and Engineering to get the application area courses approved before taking such courses. General Elective credits may be needed to meet the 128 credits required depending on chosen Application Area.

Advertising

  • course Introduction to Advertising (4) 

  • course Advertising Agency Workshop (4) 
    or course Advertising Creative Strategy (4) 


Cybercrime

  • course Introduction to Criminal Justice (4) 

  • course Cybercrime (4) 

Economics

  • course Intermediate Macroeconomics (3) 

  • course Economics of Energy and the Environment (3) 
    or course International Economic Development (3) 
    or course Economics of Health Care (3) 

Prerequisites for the Economics Application Area: course and course

Environment

  • course Energy and the Environment (4) 
    or course Public and Environmental Health (3) 
    or course Global Environmental Governance (4) 

  • course Geographic Information System Analysis for Sustainability (4) 

Genomics

  • course Genetics (4) 

  • course Genetic and Genomic Engineering (4) 
    or course Advanced Genetics (4) 
    or course Functional Genomics and Bioinformatics (4) 

Prerequisite for the Genomics Application Area: course 

Health

  • course Introduction to Health and Health Behaviors (3) and course Introduction to Health and Health Behaviors Learning Lab (1) 

  • course Sociology of Health and Medicine (4) 
    or course Introduction to Public Health (3) 
    or course Public Health Program Implementation (4) 
    or course Population Health, Health Policy, and Healthcare Delivery (4) 


Malware Detection

  • course Introduction to Computer Networks (4) 

  • course Information Security (4) 
    or course Information Security Practices (4) 


Politics
Choose 2 courses:

  • course Media and Politics (4) 
    or course Elections and Voting Behavior (4) 
    or course Public Opinion (4) 
    or course Politics and the Internet (4) 


Risk Management

  • course Insurance and Risk Management (3) 

  • course Financial Accounting (4) 
    or course Foundations of Safety Engineering (4) 
    or course Engineering Risk Analysis (4) 

Prerequisite for the Risk Management Application Area: course.

Supply Chain Management
Choose 2 courses:

  • course Supply Chain Modeling and Analysis (4) 
    or course Operations Management (3) 
    or course Supply Chain Management (3) 
     

General Electives

Student must complete a minimum of 2 additional credits in general electives.

Data Science and Computer Science Double Major

Students interested in pursuing a double major in Data Science and Computer Science are encouraged to consult with an academic adviser for a nine-semester course plan.

Optional Concentration in Artificial Intelligence

The Department of Computer Science and Engineering offers an optional concentration in Artificial Intelligence to students interested in broadening their knowledge in this specific area of Data Science and wishing the area of concentration in their degree. The concentration is available to, but not required of, any student enrolled in the Bachelor of Science degree in Data Science. The concentration will be noted on the transcript of the students. The concentration must be completed as part of their degree. To complete the concentration with 128 credits, students should strategically select an application area course also to count as a General Education course. Students interested in the concentration should consult an academic adviser for guidance on course selection. Please refer to the concentration requirements for more details.

Required subjects

Take the following four courses

  • course Artificial Intelligence (4) 

  • course Deep Learning and Applications (4) 

  • course Machine Learning (4) 

  • course Natural Language Processing (4) 

Major Standing

To enroll in 3000- or higher level courses and to become candidates for the degree of Bachelor of Science with a major in Data Science, students must gain major standing. An application for major standing should be submitted prior to intended enrollment in 3000- or higher level courses. Students can obtain the major standing form from the SECS Undergraduate Advising Website. When the application for major standing is approved, students with majors of Pre-Data Science will have their major changed to Data Science. Approval of both a major standing application and change of major to Data Science is required prior to enrolling in any 3000- or higher-level courses.

To gain major standing in Data Science, students must:

• have a minimum average GPA of 2.0 in major standing courses which consist of course, course, course, course, course, course, and course;

• have no more than 2 grades with C-, D+, or D in the major standing courses;

• have not attempted any major standing course more than 3 times; and

• have not repeated more than 3 different major standing courses, with courses bearing a W (withdrawal) grade not being counted.

Conditional major standing, which permits students to register for 3000- or 4000-level SECS courses, will be granted in the semester during which the student will fulfill requirements for major standing courses.

Students who have questions about petition of exception, transfer credit, academic standing, major standing, or any other aspects of their degree programs should consult an academic adviser and other relevant sections of the undergraduate catalog.

Performance Requirements

Satisfactory completion of the program requires an average grade of at least 2.0 within each group: quantitative foundations and approved science elective; data science core; professional courses (including required professional subjects, professional electives, and application area courses). Within the professional courses at most 2 different courses may be repeated, a total of 3 attempts per course is permitted, and at most 2 grades below C are permitted. A grade of C or better in course (Data Science Capstone) must be received.

Sample Data Science Schedule

Students entering the School of Engineering and Computer Science with the required background may follow a schedule such as the one indicated below. However, students will need additional time to complete the program if they do not have the required background upon entrance to the program.

Freshman Year

Fall Semester - 16 credits

  • course Introduction to Python Programming and Unix (4) 

  • course Calculus I (4) 

  • General Education (4)

  • General Education (4)
     

Winter Semester - 16 credits

  • course Object-Oriented Computing (4) 

  • course Calculus II (4) 

  • General Education (4)

  • General Education (4)

Sophomore Year

Fall Semester - 16 credits

  • course Discrete Structures with Applications (4) 
    or course Discrete Mathematics (4) 

  • course Introduction to Data Science in Python (4) 

  • Approved Science Elective (4)

  • General Education (4)
     

Winter Semester - 16 credits

  • course Data Structures (4) 

  • course Computational Linear Algebra (4) 
    or course Linear Algebra (4) 

  • course Applied Probability and Statistics (4) 

  • General Education (4)

Junior Year

Fall Semester - 15 credits

  • course Database Design and Implementation (4) 

  • course Design and Analysis of Algorithms (4)    

  • course Contemporary Issues in Data Science (3) 

  • General Education (4)
     

Winter Semester - 17 credits

  • course Data Visualization (3) 

  • course Security and Privacy in Computing (4) 

  • course Applied Linear Models I (4) 
    or course Statistical Methods in Data Science (4) 

  • Application Area (4)

  • General Electives (2)

Senior Year

Fall Semester - 16 credits

  • course Introduction to R for Data Science (4) 

  • Application Area (4)

  • Professional Elective (4)

  • Professional Elective (4)
     

Winter Semester - 16 credits

  • course Information Retrieval and Knowledge Discovery (4) 

  • course Big Data Analysis with Cloud Computing (4) 

  • course Data Science Capstone (4) 

  • Professional Elective (4)

Applicable Minors

All Minors are applicable to this major with the exception of the following Minor(s): Child Welfare, Computer Science, Data Science, and Information Technology.