The MS in Information Science: Machine Learning is a two-year, hybrid master’s program offered by the University of Arizona. It combines online instruction during the first year with on-campus classes in Tucson, Arizona, during the second year.
The program is structured to accommodate international students and provides access to the university’s campus resources, faculty, and career services.
Curriculum and Learning Outcomes
The program covers core areas such as machine learning, neural networks, data mining, data visualization, cloud analytics, and applied natural language processing. Students also complete a capstone project that applies learned concepts to real-world challenges.
The curriculum is designed to support both foundational and advanced learning, making it accessible for students new to the field.
Foundations of Information
Covers key concepts in managing and preparing data for analysis. Topics include types of data, extraction and transformation techniques, data usability practices, and storage systems such as SQL, NoSQL, Hadoop, and Spark.
Data Mining and Discovery
Introduces techniques for identifying patterns in data and understanding dataset characteristics. Includes methods for business problem scoping, graphical analysis, supervised learning (e.g., decision trees), and unsupervised learning like clustering and association rules.
Data Analysis and Visualization
Focuses on visual representation of data using design principles, multidimensional graphics, and audience-specific approaches. Emphasizes interactive visualizations with tools like D3.js and Python libraries.
Introduction to Machine Learning
Provides a comprehensive look at ML techniques including linear and non-linear modeling, classification (e.g., logistic regression, SVM), and tree-based methods like Random Forest and Gradient Boosting.
Data Warehousing and Analytics in the Cloud
Covers database modeling, SQL querying, data warehousing architecture, ETL processes, and Azure-based cloud data management. Emphasizes practical skills in managing and analyzing large-scale data.
Data Ethics
Examines ethical responsibilities in data collection, modeling, and usage. Topics include privacy preservation, algorithmic bias, compliance standards like GDPR, and maintaining ethical standards in AI development.
Neural Networks
Explores the structure and training of neural networks including CNNs, RNNs, and Transformers. Addresses regularization, model debugging, and tools for improving model transparency and interpretability.
Applied Natural Language Processing
Covers modern NLP techniques with neural architectures like GPT and Transformers. Includes text acquisition, classification, sequence tasks, and prompt engineering using pre-trained language models.
Advanced Machine Learning Applications
Applies deep learning to real-world AI use cases such as computer vision and generative models. Includes diffusion models, text-to-image generation, and industry-specific AI deployment examples.
Capstone Project
Students work on applied projects aligned with university research themes. Sample topics include climate data forecasting, public health NLP, and population health imaging using AI techniques.
More curriculum info here:
Credits, Duration, and Cost
This full-time program requires completion of 30 credits over two years. The total tuition is approximately $35,676.
Outcomes and Career Opportunities
Graduates of the program are prepared for roles such as data scientist, machine learning engineer, information security analyst, and computer research scientist. Reported average salaries for these positions range from $100,000 to $136,000 per year. Graduates are eligible for a 3-year STEM OPT visa in the U.S., enabling post-study employment.
Requirements and Application
Academic Background
- Applicants must have one of the following:
- A 4-year accredited bachelor’s degree
- A 3-year accredited bachelor’s degree plus a master’s degree
- A 3-year accredited bachelor’s degree plus a postgraduate diploma
- A 3-year bachelor’s degree alone, if it includes at least 120 credits and was awarded by an institution with a NAAC rating of ‘A’ or higher
Language Proficiency
- GRE and TOEFL scores are not required for admission
- English proficiency tests are not required for students from India
- TOEFL or equivalent may be required only for students hired as Teaching Assistants
Academic Readiness
- No prior programming experience is needed
- Great Learning provides pre-course resources and in-program support for those without a technical background
Application Process
- Submit a short online application form
- Complete a pre-screening call to confirm eligibility
- Wait for formal review by the admissions team
- If accepted, receive an offer letter with payment instructions
Additional Notes
Attendance at live sessions is recommended but not mandatory; all sessions are recorded and available online
Applicants can take a one-year break between years via a formal Leave of Absence request
Who It’s For
Early-Career Professionals:
Individuals who recently entered the job market and want to specialize in machine learning or data science without prior work experience in the field.
Mid- and Senior-Level Professionals:
Managers, analysts, or technical leads aiming to stay current with evolving machine learning technologies and apply them in leadership roles.
Final-Year Students:
Undergraduate students in their last semester who are looking to study abroad and start strong with a specialized master’s degree in machine learning.
Career Changers:
Professionals from other fields seeking to shift into AI, machine learning, or data science, particularly those looking for a program that doesn’t require GRE or prior coding skills.
International Students Seeking U.S. Work Experience:
Those who want access to U.S. job markets post-graduation and seek a degree that qualifies for a 3-year STEM OPT work visa.
Delivery and Support
While not fully online, the hybrid format offers flexibility in the first year and immersive, in-person learning in the second year. Students benefit from mentorship, hands-on projects, alumni networking, and access to top employers such as Google, Microsoft, Tesla, and NASA.