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Programme Details Page dev

Overview

The Bachelor of Science (B.Sc.) in Computer Science allows students to pursue a concentrated study of computer scienc e, along with another discipline (the “Computing and X” model). Students will study the foundations of computing, including mathematics, programming, computing theory, operating systems and networking. These foundations are complemented by the knowledge accumulated in other disciplines such as biology, geographic information systems, physics, mathematics, or chemistry. The result is a unique degree featuring strength in computing, breadth in general education, and a focus on a particular discipline of interest.

Program Learning Outcomes

  • Apply entrepreneurial thinking, leadership, and data science skills to identify, analyze, and address global challenges and opportunities, leading self and others toward innovative solutions.
  • Illustrate the process of new venture creation.
  • Apply quantitative methods to analyze and solve complex problems.
  • Apply critical and ethical thinking across different domains of knowledge, demonstrating strong written, oral, and technology based communication skills.
  • Demonstrate proficiency in data manipulation, statistics, and data modeling using tools such as SQL, Power BI, and Python.
  • Showcase the ability to clean, analyze, and visualize data effectively, and construct efficient data-driven models.
  • Create a portfolio that showcases career-ready work, reflecting current knowledge and practice in data science.

Program Length

The Bachelor of Science in Data Science requires the completion of 120 credits. The degree program is designed to be taken in a full-time, year-round manner, allowing it to be completed in three (3) years. However, this duration may vary depending on individual course progression and any prior credits transferred. The time limit for completing the degree program is eight (8) years.

Degree Requirements

The Bachelor of Science in Data Science is comprised of three content areas: general education courses (36 semester credits); data science and software engineering courses (57 semester credits); and entrepreneurship courses (27 semester credits).
Students must successfully complete all required courses with a passing grade.

Course List

View Course Descriptions

Data Science/Software Engineering Courses

AWS 400

AWS Cloud Computing

DS 100

Introduction to Data Science

DS 120

SQL for Data Science

DS 130

Data Visualisation

DS 200

Python for Data Scientists I

DS 300

Techniques for Regression Analysis

DS 320

Natural Language Processing and Classification

DS 400

Unsupervised Learning Methods

DS 440

Portfolio Review

DS 400

Unsupervised Learning Methods

SE 101

Introduction to Computing

SE 102

Foundations of Linux and Version Control

Entrepreneurship Courses

BUS 200

Business Finance

ENT 100

Foundations of Entrepreneurship

ENT 110

Introduction to Venture Creation

ENT 300

Ethics and Technology

ENT 310

Leadership and Management

ENT 400

Special Topics

General Education Courses

ART 200

Principles of Design & Media

COM 148

Communication for Impact

PE 101

Intro to Personal Effectiveness

PE 301

Applied Personal Effectiveness

PE 401

Personal Effectiveness for Career Readiness

QNT 101

College Algebra

QNT 102

Statistics

QNT 105

Foundations of Data Analysis and Decision Making

SCI 200

Introduction to Climatology, Ecology, and Human Impact

SS 200

Introduction to Sociology: Gender Inequality, Women Empowerment, and Education

SS 300

Consumerism in Society

SS 360

Research Methods in Social Sciences

WR 100

Fundamentals of Effective Communication

WR 300

Advanced Business Communication

Final Projects

As part of students’ fulfillment of their degree requirements, they are required to assemble and defend a portfolio of work in DS 440 Portfolio Review. The learning outcomes for this are to:

  1. Present, reflect, and iterate on a portfolio of data science challenges and solutions which demonstrate career readiness.
  2. Create a resume that demonstrates career readiness.
  3. Exhibit entry-level readiness by completing tasks related to SQL database management, Phyton data analysis using Pandas, data visualization with Power BI, database manipulation, data analysis, and the creation of insightful visualizations.
  4. Exhibit entry-level career readiness by demonstrating machine learning proficiency, emphasizing regression, classification, interpretation of model parameters, evaluation metrics and the application of algorithms including linear regression, logistic regression, decision trees, random forests, and support vector machines.

Tuition Fees

Tuition:

$1,333 per term (full-time course load required) or $100 per credit with prior approval only.

Total estimated charges (120 credits, 9 terms):

$12,000
USD
Total charges may vary based on repeated courses, transfer of credit or advanced standing, and/or time to completion.

DEGREE

Bachelor of Science (B.S c.)

FORMAT

Online Full-Time

CREDITS

120

DURATION

3 years