Work-Integrated Graduate Program

Master's Degree in Computer Science

A two-year master's degree in computer science, designed so that learning and real work happen at the same time. Available with a paid apprenticeship that covers the cost of your degree and puts you ahead.

$1k/mo
Net Income*
2 years
Apprenticeship
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Overview

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Program Length

2 years

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Study Load

20hrs/week

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Work Load

20hrs/week

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Delivery Mode

Flexible, on-demand curriculum
Weekly live sessions
Personalized mentor feedback

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Concentrations

Artificial Intelligence
Data Science
Software Engineering

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Costs

Tuition: $34,00
Scholarship: $10,00
Earnings: $48,000
Net Income: $1,000/mo

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Faculty Ratio

8:1 Student to Instructor Ratio
1:1 Student to Mentor Ratio

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Skill Level

Bachelor's degree

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Start Dates

The first Monday of September, January, and June

The Work-Integrated Model

Degree and Experience

Not One or the Other

Most education programs make you choose. You can go to school full-time and graduate with a credential, but nothing on your resume. Or you can work and build experience while your education waits. Clarke's work-integrated model is built around a third option.
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Every work-integrated program pairs rigorous coursework with a paid apprenticeship that runs alongside it. You learn the material and apply it in a real working environment at the same time. By the time you graduate, you have not just studied the field. You have worked in it.
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The apprenticeship is also structured to offset the cost of your education. Your scholarship and apprenticeship earnings more than cover your tuition.

Don't go into debt to get a serious credential.

What You'll Learn

The Master's in Computer Science runs in three phases over two years. You will go deep in advanced computer science, specialize in your chosen concentration, and conclude with a capstone that brings everything together.

Not ready for a full degree? Check out the Career Pathways

Program Outline


2 years

Project-based learning

Flexible, online format

Work-integrated design

Industry mentorship

Instruction from a practitioner

Apply Now
Tier 1: Foundations
  • Mathematics for Computer Science
    ‍    Develop the discrete mathematics foundation that underlies algorithm design, systems analysis, and software engineering. You will work through asymptotic notation, permutations, combinations, discrete probability, and recurrences, with consistent emphasis on applying these tools to practical computing problems. Catalog→
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  • Design and Analysis of Algorithms
    ‍    Develop the ability to design efficient algorithms and evaluate their performance against real-world constraints. You will work through divide and conquer, dynamic programming, greedy strategies, backtracking, and randomized approaches, building the analytical foundation needed for system design and advanced engineering work. Catalog→
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  • Relational Databases
    ‍    Learn to design, implement, and query relational database systems for real-world data storage needs. You will move from entity-relationship modeling through schema design, SQL query development, normalization, and performance optimization, deploying and managing databases in cloud environments using Amazon RDS. Catalog→‍
Tier 2: AI Concentration
  • DevOps
    ‍    Build the skills to deploy applications reliably and at velocity on distributed infrastructure. You will work through Linux scripting, Docker, Kubernetes, CI/CD pipelines with Jenkins, and automated testing, covering serverless compute, infrastructure-as-code, and cloud DevOps, developing the operational judgment needed to keep production systems healthy at scale. Catalog→
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  • Numerical Programming in Python
    ‍    Learn to translate mathematical and scientific concepts into efficient Python code. You will build a working foundation in Python and then go deep into NumPy, SciPy, and Pandas, the core libraries that power data science and machine learning work, preparing for rigorous ML and data science coursework throughout the program. Catalog→
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  • Applied Statistics
    ‍    Develop the statistical foundations needed to perform mathematically rigorous data analysis. You will work through probability theory, discrete and continuous distributions, hypothesis testing, and A/B experiment design, with applied computational tools including bootstrapping and Monte Carlo methods grounded in real-world examples. Catalog→
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  • Introduction to Machine Learning
    ‍    Build a rigorous foundation in classical machine learning, mathematically and practically. You will work through logistic regression, gradient descent, decision trees, naive Bayes, and k-nearest neighbors, developing both the theoretical understanding and hands-on Python skills needed to move confidently into advanced ML work. Catalog→
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  • Distributed Machine Learning
    ‍    Learn to train and deploy ML models on distributed infrastructure at petabyte scale. You will work through MapReduce, Spark, SparkMLLib, and TensorFlow's distributed computing architecture, building the skills to run data processing and model training across CPU, GPU, and TPU clusters. Catalog→
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  • Deep Learning for NLP
    ‍    Learn to model sequences including text, music, time series, and genes using modern deep learning architectures. You will work through RNNs, LSTMs, attention mechanisms, and Transformer-based models including BERT and GPT, with applied projects covering question-answering systems, conversational agents, and semantic search. Catalog→
Tier 2: Data Science Concentration
  • SQL for Data Analytics
    ‍    Develop the SQL depth needed for serious data analytics work. You will move well beyond basic queries to window functions, CTEs, query optimization, and the analytical patterns used by data professionals to answer complex business questions at scale, working across both traditional databases and cloud platforms, including BigQuery and Redshift. Catalog→
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  • DevOps
    ‍    Build the skills to deploy applications reliably and at velocity on distributed infrastructure. You will work through Linux scripting, Docker, Kubernetes, CI/CD pipelines with Jenkins, and automated testing, covering serverless compute, infrastructure-as-code, and cloud DevOps, developing the operational judgment needed to keep production systems healthy at scale. Catalog→
    ‍
  • Numerical Programming in Python
    ‍    Learn to translate mathematical and scientific concepts into efficient Python code. You will build a working foundation in Python and then go deep into NumPy, SciPy, and Pandas, the core libraries that power data science and machine learning work, preparing for rigorous ML and data science coursework throughout the program. Catalog→
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  • Applied Statistics
    ‍    Develop the statistical foundations needed to perform mathematically rigorous data analysis. You will work through probability theory, discrete and continuous distributions, hypothesis testing, and A/B experiment design, with applied computational tools including bootstrapping and Monte Carlo methods grounded in real-world examples. Catalog→
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  • Introduction to Machine Learning
    ‍    Build a rigorous foundation in classical machine learning, mathematically and practically. You will work through logistic regression, gradient descent, decision trees, naive Bayes, and k-nearest neighbors, developing both the theoretical understanding and hands-on Python skills needed to move confidently into advanced ML work. Catalog→
    ‍
  • Distributed Machine Learning
    ‍    Learn to train and deploy ML models on distributed infrastructure at petabyte scale. You will work through MapReduce, Spark, SparkMLLib, and TensorFlow's distributed computing architecture, building the skills to run data processing and model training across CPU, GPU, and TPU clusters. Catalog→
Tier 2: Engineering Concentration
  • Computer Systems and Their Fundamentals
    ‍    Develop a working understanding of the three systems that underpin all software: databases, operating systems, and computer networks. You will move from distributed data storage and concurrency through process scheduling and memory management, and close with a detailed walkthrough of all computer network layers and their protocols. Catalog→
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  • Front End Development
    ‍    Master the frameworks that power modern front end development. You will work through React.js, React Native, jQuery, and AngularJS, building production-quality interfaces and cross-platform applications, developing the architectural thinking needed to design real-world front ends rather than just write components. Catalog→
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  • Back End Development
    ‍    Build server-side applications using Node.js and the event-driven programming model that powers scalable web backends. You will work from Node.js fundamentals through REST API development and database integration, building real backend systems and learning Linux server administration and monitoring. Catalog→
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  • Practical Software Engineering
    ‍    Apply low-level design skills to real-world engineering problems through detailed case studies. You will work through the complete design process for real systems, developing the ability to move from a problem statement to a code-ready design document and the engineering judgment that distinguishes strong software engineers in professional settings. Catalog→
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  • Data Engineering
    ‍    Learn to process and move data at scale. You will work through the complete data engineering lifecycle, from distributed processing fundamentals through data warehousing design and ETL pipeline construction for both batch and streaming data, building the infrastructure organizations depend on to run ML systems. Catalog→
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  • Product Management for Software Engineers
    ‍    Develop the product thinking that makes engineers more effective contributors and stronger candidates for leadership roles. You will work through the complete product management lifecycle, from customer discovery through launch and analytics, building the ability to translate evolving user needs into well-scoped technical work. Catalog→
Tier 3: Capstone
  • Advanced Applied Computer Science Capstone
    ‍    The culminating experience of the MS in CS program. You will identify a technically challenging real-world computational problem, conduct a rigorous literature survey, develop a system design, and implement and deploy a complete end-to-end solution, documenting your work in a detailed technical report and presenting to faculty and external evaluators. Catalog→

The Economics

The Apprenticeship

When you enroll in the Master's program, you're invited to apply to the Bletchley Fellowship, a nonprofit that works alongside Clarke and employers to enable our work-integrated education model. Fellows receive the Bletchley Scholarship, $10,000 applied directly to their tuition. The Fellowship also handles apprenticeship matching, connecting you with an employer on day one.
The apprenticeship pays approximately $48,000 over the course of the program. When you combine the scholarship and the apprenticeship earnings, most fellows net around $1,000 per month while they're in the program.
Learn More About the Bletchley Fellowship

Costs

Tuition: $34,000
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Scholarship: $10,000
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Earnings: $48,000
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Net Income: $1,000/mo
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Built for You

The Master's in Computer Science is built for people who already have a technical foundation and are ready to go deeper. This is not a traditional graduate program. It is a two-year commitment to advanced technical work, with a paid apprenticeship running alongside your studies from month one.

This program is a strong fit if you:

●   Already have a bachelor's degree in computer science or equivalent professional experience

●   Want to specialize in AI, data science, software engineering, or cybersecurity (coming soon)

●   Want a graduate degree that pays for itself and puts you ahead financially

●   Want to advance your career with a credential that reflects serious technical depth

"I was skeptical that a two-year program could be both rigorous and work-integrated. It is."

The coursework is demanding and the apprenticeship adds to that load. But the two reinforce each other in a way I did not anticipate. What I learned in class showed up in my work the same week and vice versa.

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Laura Gifford
Louisville, KY
"I had years of engineering experience. What I needed was the credential and the specialization to go with it."

The Master's program gave me both without asking me to stop working. The apprenticeship ran alongside my coursework from day one and the depth of the curriculum pushed me in ways I did not expect.

Young man with blond hair wearing a dark blue henley shirt sitting outdoors at a metal table with blurred green trees and buildings in the background.
Brian Calloway
Las Vegas, NV
"I looked at traditional master's programs and could not justify two years of tuition with no income."

The economics here are different. I earned while I studied, finished with no debt, and came out ahead. That is not something most graduate programs can say.

Young woman with blonde hair in a side braid wearing a white sweater, seated in front of a bookshelf.
Stephanie Norris
San Antonio, TX
"The cybersecurity concentration gave me a specialization I could not have built on my own."

I had a solid engineering background but security was always something I worked around rather than owned. This program changed that. I finished with real depth and a portfolio of security work that employers could verify.

Young man with short hair wearing a green jacket outdoors with leafy background.
Daniel Marsh
Philadelphia, PA
"I was skeptical that a two-year program could be both rigorous and work-integrated. It is."

The coursework is demanding and the apprenticeship adds to that load. But the two reinforce each other in a way I did not anticipate. What I learned in class showed up in my work the same week and vice versa.

Sophie Moore Avatar Eduhub X Webflow Template | Brix Template
Laura Gifford
Louisville, KY
"I had years of engineering experience. What I needed was the credential and the specialization to go with it."

The Master's program gave me both without asking me to stop working. The apprenticeship ran alongside my coursework from day one and the depth of the curriculum pushed me in ways I did not expect.

Young man with blond hair wearing a dark blue henley shirt sitting outdoors at a metal table with blurred green trees and buildings in the background.
Brian Calloway
Las Vegas, NV
"I looked at traditional master's programs and could not justify two years of tuition with no income."

The economics here are different. I earned while I studied, finished with no debt, and came out ahead. That is not something most graduate programs can say.

Young woman with blonde hair in a side braid wearing a white sweater, seated in front of a bookshelf.
Stephanie Norris
San Antonio, TX
"The cybersecurity concentration gave me a specialization I could not have built on my own."

I had a solid engineering background but security was always something I worked around rather than owned. This program changed that. I finished with real depth and a portfolio of security work that employers could verify.

Young man with short hair wearing a green jacket outdoors with leafy background.
Daniel Marsh
Philadelphia, PA
"I was skeptical that a two-year program could be both rigorous and work-integrated. It is."

The coursework is demanding and the apprenticeship adds to that load. But the two reinforce each other in a way I did not anticipate. What I learned in class showed up in my work the same week and vice versa.

Sophie Moore Avatar Eduhub X Webflow Template | Brix Template
Laura Gifford
Louisville, KY
"I had years of engineering experience. What I needed was the credential and the specialization to go with it."

The Master's program gave me both without asking me to stop working. The apprenticeship ran alongside my coursework from day one and the depth of the curriculum pushed me in ways I did not expect.

Young man with blond hair wearing a dark blue henley shirt sitting outdoors at a metal table with blurred green trees and buildings in the background.
Brian Calloway
Las Vegas, NV
"I looked at traditional master's programs and could not justify two years of tuition with no income."

The economics here are different. I earned while I studied, finished with no debt, and came out ahead. That is not something most graduate programs can say.

Young woman with blonde hair in a side braid wearing a white sweater, seated in front of a bookshelf.
Stephanie Norris
San Antonio, TX
"The cybersecurity concentration gave me a specialization I could not have built on my own."

I had a solid engineering background but security was always something I worked around rather than owned. This program changed that. I finished with real depth and a portfolio of security work that employers could verify.

Young man with short hair wearing a green jacket outdoors with leafy background.
Daniel Marsh
Philadelphia, PA

Questions?

We Have Answers

What experience do I need to apply?

You need a bachelor's degree in computer science or equivalent professional experience. If you have been working as a software engineer, data analyst, or in a related technical role and have not completed a formal CS degree, the admissions team will assess your background as part of the process. Strong practical experience can satisfy the entry requirement.

Do I have to do the apprenticeship?

No. The Master's in Computer Science is available with or without the apprenticeship component. If you are already working in a technical role, you can pursue the academic track and apply what you are learning directly in your current job. If you want the work-integrated track, you will be invited to apply to the Bletchley Fellowship, which handles placement and funds the scholarship that makes the financial model work.

How does the apprenticeship work?

Once accepted to Clarke and matched by the Bletchley Fellowship, your apprenticeship begins in month one alongside your coursework. It runs at 20 hours per week for most of the program, stepping up during the capstone phase. The scholarship and apprenticeship earnings are structured so that your tuition is fully covered and you net approximately $1,000 per month for the duration of the program.

What concentrations are available?

The Master's in Computer Science offers four concentrations: AI, Data Science, Software Engineering, and Cybersecurity (coming soon). Each builds on a shared foundation of advanced computer science before going deep in the chosen discipline. Your apprenticeship work is aligned with your concentration throughout the program.

Is this degree accredited?

Yes. Clarke College is a member college of Woolf, a globally recognized and fully accredited European collegiate institution. Your degree is portable and verifiable, the same as any other accredited institution.

What kind of jobs will I be prepared for?

Graduates pursue roles as AI Engineers, Data Scientists, Software Engineers, Data Engineers, Security Engineers, and Penetration Testers. All graduates finish with two years of apprenticeship experience that employers can verify, alongside their graduate credential.

Is this program open to international students?

Clarke's academic programs are open to students regardless of citizenship or work authorization. However, the Bletchley Fellowship and apprenticeship component require that students be authorized to work in the United States without employer sponsorship. If you are not currently authorized to work in the US, the program is available to you as an academic-only program without the apprenticeship component.

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Pick the start date that fits your schedule.

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