This track focuses on Machine Learning, preparing students with the technical knowledge and practical skills the field expects at the Associate-to-Bachelor's level.
Graduates are equipped to pursue relevant technology career paths, with a self-paced format built for students balancing work and other commitments.
CS-026Introduction to E-Commerce
Covers the fundamentals of buying, selling, and transacting online.
- Online business models
- Digital payment processing basics
- E-commerce platform fundamentals
CS-159Introduction to Data Communication
Introduces how data is transmitted and structured across communication networks.
- Data transmission basics
- Communication protocols overview
- Signal types and encoding
CS-172Introduction to Computing
A foundational look at computer systems, hardware, and basic computing concepts.
- Computer hardware basics
- Operating system fundamentals
- File systems and storage
CS-366Networking Basics
Covers the fundamental building blocks of computer networks.
- Network topology basics
- IP addressing fundamentals
- Common networking hardware
CS-535Introduction to Programming
Introduces core programming logic and syntax used across languages.
- Variables and control structures
- Writing basic functions
- Debugging simple programs
CS-580Essentials of Computer Science
Covers the foundational concepts every computer science student needs.
- Core CS terminology
- Problem-solving approaches in computing
- Overview of CS career paths
CS-633Foundations of Information Technology
Introduces the fundamentals of managing and supporting IT systems.
- IT support fundamentals
- System maintenance basics
- Common IT troubleshooting steps
CS-675Computer & Internet Search
Builds effective research and information-retrieval skills using digital tools.
- Advanced search techniques
- Evaluating online sources
- Organizing digital research
2
Specialist Courses
6 Courses
FOM-2440Fundamentals of Machine Learning
Covers the foundational concepts behind training predictive models.
- Supervised vs unsupervised learning
- Basic model training concepts
- Common machine learning use cases
ITM-2441Introduction to Machine Learning
A broad orientation to the machine learning field and career paths.
- History of machine learning
- Machine learning career pathways
- Overview of ML tools and platforms
MLP-2442Machine Learning Principles
Introduces the mathematical and statistical principles behind ML models.
- Statistical foundations of ML
- Model evaluation principles
- Bias and variance basics
MLA-2466Machine Learning Analysis
Covers techniques for analyzing and interpreting machine learning model results.
- Interpreting model performance metrics
- Analyzing prediction errors
- Feature importance analysis
MLA-2467Machine Learning Applications
Explores applying machine learning models to real-world business problems.
- ML in business decision-making
- Applying ML to real datasets
- Deploying models to production
MLS-2468Machine Learning Strategies
Covers strategic approaches to adopting machine learning within an organization.
- ML adoption roadmap planning
- Choosing the right ML approach
- Long-term model maintenance planning
$24,000
Total Program Cost
Fees are billed per term and are subject to change. Financial aid, scholarships, and flexible payment plans are available to students who qualify.
Explore Financial Aid
Admission requires Associate Degree or equivalent credits.
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Submit Your Application
Complete the online application with your basic and academic details.
An admissions advisor reviews your application and any transfer credit.
Receive your decision, complete enrollment, and start your first term.
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