Program Overview

Master of Science in Applied Analytics

The Master of Science in Applied Analytics is a specialized master's degree that leverages the power of data analysis, machine learning, and artificial intelligence to solve real-world problems in various industries. This STEM-designated program is taught by experienced professionals and prepares students to apply advanced analytical approaches to drive business and policy decisions.

At a Glance

• The program consists of 10 courses: two foundational, four core courses, three electives, and an AI practicum.
• Students can complete the degree in as little as 12 months of full-time study or 20 months of part-time study.
• Flexible scheduling allows students to study full- or part-time online, on campus during the evening, or in any combination that works for them.
• The total cost of the M.S. degree is $44,700, with a tuition rate of $1,490 per credit for the Academic Year 2025–2026.

What Sets Us Apart

• Strong Faculty: Our faculty have considerable industry experience directly related to the course they teach.
• Advisory Board: Our active advisory board consists of leaders in the field who help shape our curriculum, mentor students, and provide placement opportunities.
• Engagement Model: We limit class size to foster faculty engagement with students.
• Academic and Career Services: We provide strong academic advising, support, career coaching, and organize recruiting events.
• Applicable Skills: We teach in-demand skills and infuse the curriculum with communication and non-technical skills necessary for success outside the classroom.
• Applied Analytics Project: Students deliver an AI project from start to finish, practicing technical tools, project management, and communication skills.

Curriculum

Foundational Courses

These courses establish the necessary background for further study in the field. Students who have taken comparable courses in their undergraduate program can waive these courses and take electives instead.

• Mathematical Methods for Machine Learning I: Covers foundational methods in linear algebra and vector calculus to understand the structure and dimensionality of large and complex datasets.
• Data Analysis: Introduces students to the concepts and data-based tools of statistical analysis commonly employed in Applied Economics.

Core Courses

These courses allow students to develop the competencies necessary to conduct analytical work and apply it in the real world.

• AI/ML Software Tools and Platforms: Prepares students to understand the data engineering required for data science research projects and industry products.
• AI Algorithms I / Big Data Econometrics: Demonstrates how to merge economic data analysis and applied econometric tools with machine learning techniques.
• AI Algorithms II: Teaches advanced AI algorithms, covering neural networks, deep learning architectures, and reinforcement learning.
• Algorithmic Ethics and Governance - from traditional to AI/ML: Surveys governance frameworks and techniques for algorithms used to make decisions within an organization or in servicing clients.

Project Course

• Applied Analytics Project: All students must complete this project, obtaining end-to-end experience in building an analytical solution to a business or policy problem.

Electives

Students choose at least three electives to customize their learning and fit their objectives. Electives focus on advanced topics in mathematics and analytics or help students practice their skills in business areas.

• Regression Models/Econometrics
• Machine Learning Product Management
• Predictive Analytics/Forecasting
• Mathematical Methods for Machine Learning II
• Operations Research
• Data Visualization and Communication
• Computer Vision
• Natural Language Processing

Dual M.S. in Applied Economics/M.S. in Applied Analytics Degree

A dual degree provides students with an opportunity to obtain a dual Master's degree by completing 15 courses, deepening their expertise in both Economics and Analytics.

Learning Outcomes

After completing the program, students will be able to:

• Design analytic approaches to solve complex problems
• Understand and deploy advanced analytic techniques
• Use machine learning and artificial intelligence tools for business and policy decisions
• Draw insights from analytics and communicate them clearly to non-technical audiences
• Drive real impact based on results and insights from analytics

Skills in Demand

The program develops a rich, applied skillset in four broad competency areas: Data, Technology, Business, and Soft Skills. Specific knowledge domains include:

Data

• Business Analysis
• Business Intelligence
• Data Analysis
• Data Management
• Data Modeling
• Data Visualization
• Machine Learning
• Statistics
• Data Privacy

Technology

• Hadoop
• Information Systems
• Power BI
• Programming/coding
• Python
• Spark
• SQL
• Tableau

Business

• Agile Methodology
• Business Intelligence
• Business Process
• Business Requirements
• Business Systems
• Process Improvement
• Project Management
• Business Advisory

Soft Skills

• Communication
• Influencing
• Leadership
• Management
• Planning
• Presentations
• Problem Solving
• Research
• Storytelling
• Stakeholder Management

Financial Aid

We know that a BC education is a worthwhile but significant investment. We're committed to helping you affordably achieve your educational goals. Financial aid and payment plans may be available for students taking a minimum of six credits across a semester.

Apply

Applications are accepted on a rolling basis. The application requires:

• Bachelor's degree from an accredited college/university (minimum GPA 3.0)
• Prerequisite courses: Statistics and Calculus I
• Application fee: $60
• Personal statement
• Résumé
• Letters of recommendation: Two letters, which must be sent directly from the recommender
• Transcripts: From each college or university attended
• Video essay
• Standardized tests: GRE or GMAT scores are suggested, particularly if one or more prerequisites are not met. TOEFL or IELTS scores are required for non-native English speakers.

Application Timeline

Entrance Term | Application Due Date | Decision Letter Sent By ---|---|--- Fall | Early Deadline: May 1 | May 15 | Regular Deadline: August 15 | August 30 | Rolling admissions: after Regular Deadline | Applications will be reviewed on a case-by-case basis. Spring | Early Deadline: October 15 | October 30 | Regular Deadline: January 5 | January 15 | Rolling admissions: after Regular Deadline | Applications will be reviewed on a case-by-case basis. Summer | Early Deadline: March 1 | March 15 | Regular Deadline: April 1 | April 15 | Rolling admissions: after Regular Deadline | Applications will be reviewed on a case-by-case basis.