Earn your degree in one year from a top 25 university
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Earn your degree in as little as one year from a top 25 university
No GRE required
Master of Science in Applied Analytics
Master of Science in Applied Analytics
The Master of Science in Applied Analytics (MSAA) program offers a unique and relevant opportunity to prepare professionals to advance their careers in the fields of analytics and artificial intelligence (AI). This applied learning program emphasizes the importance of a well-formulated business strategy for the appropriate use of analytics and AI to add value and contribute to business performance, providing students with the knowledge and skills needed to be effective leaders across industries. In addition to learning core concepts and mathematical and technical skills in analytics and AI, students will explore how to apply analytics methodologies and techniques to identify business opportunities and solve business problems.
Our program also equips students with skills and knowledge to lead and innovate through disruption. Students will be prepared to take on industry changes as they progress through their career.
Essential end-to-end processes of the analytics life cycle are examined and applied to real-world projects, including correctly identifying and framing business problems, acquiring and managing quality data, building analytical models, deploying the model to end users, and monitoring model performance. Students will explore practical topics that are increasingly important to analytics and AI professionals, including leadership, business acumen, data governance, data quality, ethical and responsible data usage, business strategy formulation, change management, project management, consumable data communication through visualization and storytelling, and effective communication to technical and nontechnical audiences and stakeholders.
Learning Outcomes
Assemble appropriate analytical methodologies and implementation techniques to address a business objective.
Design an ethical analytics plan to add business value.
Create a business environment conducive to the effective implementation of a scalable analytics solution.
Communicate a data-driven outcome effectively to stakeholders.
Architect an end-to-end analytics or artificial intelligence (AI) project for a business opportunity.
Generate a prototype analytics solution from model development to deployment.
Curriculum
A Master of Science degree in Applied Analytics requires 24 units of coursework offered in the fall, spring, and summer semesters. The program may be completed on a 1- or 2-year track. Students attending the program on the 1-year track can earn the degree within 12 months and those on the 2-year track can complete the program in less than two years.
Our curriculum was designed by industry leaders who are at the forefront of the future directions of artificial intelligence and applied analytics. Our engaging faculty will prepare you to be forward-thinking leaders of the profession and will give you increased confidence in your technical skills and business knowledge that will serve you throughout the rest of your career.
Applied Business Analytics and Artificial Intelligence (2 units)
Foundational principles and practical applications of analytics and artificial intelligence (AI) across various business domains with a focus on challenges involved in these techniques.
- Assess different types of data and data quality.
- Formulate a business problem as an analytical model.
- Evaluate potential ethical issues stemming from analytics and AI applications.
- Critique analytics and AI applications for specific use cases across an industry.
Statistics for Applied Data Analytics (2 units)
Statistics, probability, probability distributions, and other fundamental data analysis concepts and tools. Development of simple models to support subsequent evaluation and data-supported decisions.
- Prepare data for exploration, analysis, and modeling to drive strategic business initiatives.
- Analyze risks and forecasting outcomes using probability concepts and visualization techniques to support strategic decisions.
- Evaluate hypotheses and statistical significance across variables in various business contexts.
- Interpret biases, effects, and paradoxes in data to identify potential risks and improve the reliability of analytical conclusions.
- Synthesize statistical analyses into actionable recommendations for solving complex business problems.
Visualization and Storytelling With Data (2 units)
Tools and techniques to effectively analyze and visualize data and communicate the results. Python libraries, modules, techniques, and best practices.
- Generate actionable insights from data that inform persuasive storytelling.
- Develop a compelling narrative using data visualizations to communicate a business solution or facilitate strategic decision-making.
- Create data graphics using data visualization tools.
- Design an interactive analytics dashboard that explains a business challenge or highlights actionable insights.
Regression Modeling for Applied Predictive Analytics (2 units)
Linear regression and logistic regression models, coefficient interpretation, and performance analysis. Advanced modeling techniques.
- Build simple null models based on statistics and machine learning principles for decision-making.
- Construct a linear model for regression and classification problems to support strategic business decisions.
- Recommend solutions for common issues that arise in statistical modeling to enhance the accuracy of business insights.
- Differentiate between predictive and causal models to inform decisions across business functions.
Applied Data Management and Database Systems (2 units)
Overview of the data pipeline, data sources, data interchange formats, and the organization of data for analytics and modeling. Problems, solutions, and best practices.
- Recommend a database system for an analytics project.
- Create a database that addresses a business objective.
- Apply database query languages to organize data for analytics and modeling.
- Use best practices to ensure data integrity in keeping with data governance.
Applied Machine Learning for Business Applications (2 units)
Exploration of machine learning principles and practices using real-world data, models, and techniques. Initial data explorations to deployment to maintenance of machine learning systems.
- Analyze the complete life cycle of a machine learning project from data collection to model deployment.
- Determine an appropriate machine learning algorithm and technique to address a business problem.
- Refine a machine learning model using performance measures.
- Develop a solution to a business problem using machine learning methods and techniques.
- Improve model performance using dimensionality reduction or ensemble methods.
Applied Optimization and Simulation (2 units)
Applied mathematical programming including simulation methods, constrained optimization methods, linear programming, mixed integer programming, and statistical and discrete event simulation methods.
- Analyze business problems to select the mathematical programming or simulation method that provides the most effective solution.
- Devise an optimized approach to solving a business problem with constrained optimization.
- Develop a solution to a business problem using statistical simulation techniques.
- Formulate a strategy to address a business issue using discrete event simulation.
- Evaluate the effectiveness of programming models on a business solution.
Analytics Business Strategy and Communication (2 units)
Development of astute business acumen and strong communication skills for analytics professionals and leaders in relation to strategy, storytelling, communication, and change management.
- Develop an individual leadership style that reflects personal ethics.
- Create a business plan and financial business case for an analytics-based solution.
- Analyze change management considerations for an analytics-based solution.
- Deliver compelling communications to persuade the adoption of an analytics-based solution.
- Design an analytics plan to add business value, emphasizing ethical and governance considerations.
Applications of Deep Learning and AI in Business (2 units)
Exploration of deep learning models, deep neural networks, and artificial intelligence sequence modeling and implementation. Applications of targeted techniques to real-world business scenarios.
- Evaluate whether deep learning adds value to the core components of a business model.
- Formulate deep learning models within the context of a business problem.
- Model data in the context of neural networks.
- Distinguish between analytical techniques in the context of addressing different business objectives.
- Translate neural network results into actionable insights that solve a business problem.
Project Management for Analytics Professionals (2 units)
A comprehensive look into the analytics project life cycle from discovery through deployment. Use of appropriate metrics and best practices to monitor and validate results.
- Create a project management plan for an end-to-end applied analytics or AI project using appropriate techniques and strategies that align with business objectives and drive organizational transformation.
- Create performance metrics to measure progress and business outcomes in an analytics or AI project.
- Evaluate risk, impact, and mitigation for an applied analytics project.
- Integrate change management into an analytics or AI project.
Applications in Artificial Intelligence (2 units)
A comprehensive survey of generative artificial intelligence and artificial intelligence for decision-making; transformer and diffusion models; reinforcement learning and causal inference.
- Implement GenAI models to address a business objective.
- Design AI solutions using prompt engineering, fine-tuning, or retrieval-augmented generation (RAGs) for a business challenge.
- Analyze the latest AI advancements and their potential applications in business, staying updated with emerging trends and technologies.
- Communicate AI project decisions, processes, and analyses to stakeholders.
Applied Analytics and Artificial Intelligence Capstone (2 units)
An integrative culminating experience of the MSAA program. The practical application of knowledge and skills gained throughout the program to address a significant business problem.
- Implement a full-scale analytics and AI project from start to finish.
- Design innovative solutions with the most effective advanced analytics and AI techniques to solve a complex business problem.
- Manage a data analytics and AI project to ensure the delivery of a successful initiative throughout its lifecycle.
- Communicate complex analytical results to stakeholders.
- Develop an analytics and AI project using a mitigation plan to address ethical considerations.
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