Master of Science in Data Science

Program Overview

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Overview

Harness the Power of Data to Inform Decisions

Master the theories and applications that enable machines to learn and make predictions. Whether you want to work with big data in a corporate setting or craft your own algorithms as a technology pioneer, the master’s in data science program will equip you with the knowledge, skills, and support to achieve your professional ambitions.

You will learn how to acquire, process, analyze, and present complex data to make better management and policy decisions in industries like biotechnology, energy, quality control, transportation, telecommunications, and politics.

Program of Study

Core Requirement

• Programming for Data Science

Topological Data Analysis Courses

• Topological Data Analysis I
• Topological Data Analysis II

Machine Learning Courses

• Mathematics for Data Science
• Optimization Methods and Nonlinear Programming
• Machine Learning

Statistics Courses

• Introduction to Theory of Statistics I
• Applied Statistics
• Nonparametric Statistics

Practicum

(Choose one course)

• Practical methods in topological data analysis
• Practical methods in machine learning

Electives

(Choose two courses)

• Advanced Linear Algebra
• Introduction to Stochastic Processes I
• Additional practicum course

Capstone Requirement

The practicums require comprehensive analysis of data sets along with oral presentations or poster presentations of the results.

Career Outcomes

Nearly every industry needs insight from data science experts to solve complex business challenges, improve customer experiences, and develop innovative products and services. With an MS in Data Science degree from UAlbany, you will be at the cutting edge of a new era of intelligent data utilization.

Potential Job Titles

• Algorithm developer
• Business intelligence analyst
• Data scientist
• Data architect
• Data engineer
• Data mining
• Distributed systems engineer
• Machine learning engineer

Admissions Requirements

Deadlines

• Fall: Rolling
• Spring: Not Available
• Summer: Not Available

Required Application Materials

• Transcripts from all schools attended
• One letter of recommendation
• Statement of goals

Student Learning Objectives

• Discuss, analyze, or apply an advanced mathematical concept in the context of use in data science.
• Given a problem and the associated dataset, evaluate the required need for a model and tools needed. Build a model and solve the problem by applying appropriate learned skills, together with an appropriate code implementation.

Supplemental Degree Programs

Graduate students can typically request a supplemental degree in one or more subordinate degrees without needing to go through the full admissions process again.

Supplemental Programs Available

• Africana Studies CGS
• Community College Leadership CGS
• Demography CGS
• International Education Management CGS
• Machine Learning CGS
• Topological Data Analysis CGS

Program Outline

• Develop proficiency in essential data science tools: The curriculum focuses on equipping students with proficiency in a wide range of tools, including Python, R, TensorFlow, and topological data analysis software like Mapper, Eirene, and Ripser.
• Craft personalized learning tracks: Students work closely with faculty advisors to tailor their learning experiences to their specific career aspirations and interests, ensuring they acquire the relevant skills and knowledge for their chosen career path.

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Description:

The Master of Science in Data Science program at UAlbany offers an immersive and comprehensive educational experience. Students delve into the innovative fields of data science, machine learning, and artificial intelligence, gaining a strong foundation in the key areas of topological data analysis, machine learning, and statistics. The program is designed to cultivate critical thinking, problem-solving, and data analysis skills, empowering graduates to contribute effectively to various sectors, including biotechnology, energy, quality control, transportation, telecommunications, and politics.

• Machine Learning: The program provides a deep understanding of machine learning algorithms, enabling students to identify, build, and deploy suitable models for various data-driven tasks.
• Statistics: Students gain a solid foundation in statistical theory and methods, allowing them to effectively analyze and interpret data, drawing sound conclusions from complex data sets.

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Structure:

• Core Requirements: These foundational courses provide students with the necessary skills and knowledge to excel in data science.
• Specializations: Students can choose electives to focus on specific areas of interest, such as Machine Learning, Topological Data Analysis, and Statistics.
• Practicum: Students apply their theoretical knowledge to real-world scenarios through hands-on projects, culminating in oral or poster presentations.

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Course Schedule:

The program offers a flexible schedule with courses available during the fall, spring, and summer semesters. Students can pursue the degree full-time or part-time, allowing them to balance their academic commitments with other responsibilities.

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Modules:

• Programming for Data Science: Develops foundational programming skills in Python and R.
• Topological Data Analysis I & II: Explores the theoretical concepts and practical applications of Topological Data Analysis.
• Machine Learning: Introduces the fundamental principles of machine learning algorithms and their implementation.
• Additional Electives: Students can choose from various electives to specialize in areas of interest, including Advanced Linear Algebra, Introduction to Stochastic Processes, and Computational Methods in Statistical Inference.

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Assessment:

• Coursework: Students are evaluated through assignments, projects, and examinations within individual courses.
• Practicum: The practicum project is assessed based on its originality, depth of analysis, and presentation quality.
• Overall Performance: Students' overall academic performance is considered when evaluating their understanding and mastery of the program's objectives.

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Teaching:

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Teaching Methods:

The program employs a diverse range of teaching methods, including lectures, seminars, hands-on workshops, and individual or group projects. This varied approach ensures that students are exposed to different learning styles and can effectively grasp the complex concepts of data science.

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Faculty:

The program boasts a team of experienced and renowned faculty members who are actively engaged in cutting-edge research and possess extensive industry experience. This combination ensures that students receive instruction from highly qualified professionals with a deep understanding of both理論 and practical applications of data science.

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Careers:

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Potential Career Paths:

• Algorithm Developer
• Business Intelligence Analyst
• Data Scientist
• Data Architect
• Data Engineer
• Data Mining Engineer
• Distributed Systems Engineer
• Machine Learning Engineer

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Career Outcomes:

With the increasing demand for data science expertise across industries, graduates of the Master of Science in Data Science program at UAlbany are well-positioned for a variety of exciting career opportunities. The program's emphasis on practical skills and industry relevance ensures that graduates are equipped to make immediate contributions to organizations and drive data-driven decision-making.

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Other:

• The Master of Science in Data Science program at UAlbany is designated as a STEM program, offering international students the opportunity to apply for Optional Practical Training (OPT) and potentially extend their stay in the US for up to 36 months.
• The program offers an accelerated option for undergraduate students at UAlbany, allowing them to begin graduate coursework during their junior year, saving both time and money.
• Students have access to a dedicated Data Science team for guidance and support throughout their academic journey.
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