Online Master of Science in Data Science

The data science bootcamp curriculum is designed to provide an introduction to programming for learners of all backgrounds. It includes basic programming fundamentals and an introduction to cloud computing and data science tools.

All learners are required to complete the Data Science Bootcamp curriculum two weeks prior to the start of class. The bootcamp covers the introductory principles of data analysis and helps ensure that students are prepared to begin the program.

This module helps learners appreciate the impact of AI on all industries. Topics include incorporating AI into an overall business strategy, navigating ethical and regulatory considerations, explaining AI-driven decisions to stakeholders, emerging AI technologies and their potential impact, basic concepts of bias and fairness in AI, and privacy topics such as GDPR and personalization.

The entire online Master of Data Science program is designed to provide opportunities to put knowledge into practice. Each learner will complete a semester-long data science analysis project from a range of industries or disciplines.

Learners will demonstrate the ability to apply a wide range of data science skills acquired throughout the program, including data analysis, modeling, programming, and data management.

Learners will effectively communicate project objectives, methodologies, and results to both technical and non-technical audiences. They will also develop an impactful data science project that can be showcased to potential employers or collaborators.

This 0-credit module serves as an introduction to the program and familiarizes students with the learning technology, module structure, faculty support, and student success staff of the OMDS program.

This module focuses on the mathematical foundations of data science with a dual focus on linear algebra and probability. The linear algebra component covers vectors, matrices, tensors, multiplication, inverse, determinant, trace, and norms such as L1 and L2. The probability component covers random variables, distributions, expectation, marginal and conditional probability, independence, and correlation.

This module orients learners to programming environment setup and tools such as the filesystem, command line, and notebooks. It also reviews fundamental components of the Python programming language, including data structures, installing and importing packages, control flow, loops, and functions.

This module explores essential concepts and techniques in machine learning, including linear methods such as linear regression, lasso, and ridge; tree methods such as decision trees, random forests, and boosting; and unsupervised methods such as clustering. Learners gain proficiency fitting machine learning models, understanding regularization to prevent overfitting, and using cross-validation to evaluate model performance and tune hyperparameters.

This module provides a comprehensive exploration of data handling techniques, using case studies and real-world examples. It begins with basic SQL syntax and data manipulation and progresses to advanced SQL topics, including SELECT statements, filtering, sorting, aggregation, grouping, and joins. Learners gain proficiency in modifying data and working with database indexing, constraints, and views in SQL.

This module explores ethical considerations in data-driven decision-making and the deployment of algorithmic systems. It emphasizes the socially constructed nature of science and examines biases and inequalities. Learners explore the societal and cultural implications of AI and machine learning technologies, analyzing potential biases and disparities in data and algorithms.

Practical skills are developed through the application of fairness metrics to evaluate and mitigate bias in algorithmic decision-making. Privacy, ethical considerations surrounding personal data, and key regulatory frameworks related to AI and machine learning, such as GDPR, the California Consumer Privacy Act, and other data privacy laws, are also covered.

This module focuses on the distinction between predictive modeling and causal inference in data science. Learners gain an understanding of situations where predictive models may fall short in revealing underlying causality. Real-world examples underscore the potential pitfalls of relying on simple correlations and emphasize the need for experimentation.

Learners study foundational principles of causal inference, including potential outcomes and counterfactuals. The module also explores the principles and applications of A/B testing for assessing the impact of interventions and changes. Students develop practical skills in designing and implementing basic A/B tests, selecting appropriate metrics, and determining sample sizes.

This module explores advanced machine learning techniques and introduces the foundational concept of neural networks, emphasizing their potential as universal function approximators. Learners are exposed to optimization algorithms, with a focus on practical implementation.

The module also introduces state-of-the-art models such as transformers and large language models, exploring their roles in natural language processing and beyond. Learners complete a mini project that applies the concepts in a hands-on way.

This module explores data science challenges and opportunities in industries such as finance, health care, and e-commerce. Learners examine the unique data sources and data collection techniques relevant to their chosen sector and apply machine learning models to industry-specific needs.

Learners can further specialize by choosing a mini-concentration, such as finance, health care, or e-commerce. This allows them to apply techniques such as customer segmentation, churn prediction, and recommendation systems. A mini-project in the chosen sector rounds out the practical application of analytical skills gained in this module.