Curriculum

The data science bootcamp curriculum is designed to provide an introduction to programming for learners of all backgrounds. 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 to ensure that you are prepared to dive in on day one of the program.

This module helps learners appreciate the impact of AI on all industries. Additional 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, e.g., General Data Protection Regulation (GDPR), personalization, etc.

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 have the ability to develop an impactful data science project that can be showcased to potential employers or collaborators.

This 0-credit module will serve as an introduction to the program and familiarize students with the learning technology, mod 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 will cover vectors, matrix, tensor, multiplication, inverse, determinant, trace, and norms (L1, L2, etc.). The probability component will cover random variables, distributions, expectation, marginal/conditional probability, independence, and correlation.

This module orients learners to navigate a programming environment setup and tools like the filesystem, command line, and notebooks. Also includes a review of fundamental components of the Python programming language, including data structures (scalars, vectors, arrays, dictionaries, etc.), installing and importing packages, control flow, loops, and functions.

This module explores essential concepts and techniques in machine learning, including key topics such as linear methods (e.g., linear regression, lasso, ridge), tree methods (decision trees, random forests, boosting), and unsupervised methods like clustering. Learners will gain proficiency fitting various machine learning models, understanding regularization to prevent overfitting, and cross-validation to evaluate model performance and tune hyper-parameters.

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

This module explores ethical considerations inherent in data-driven decision-making and the deployment of algorithmic systems. Emphasizes the socially constructed nature of science, shedding light on 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 navigating key regulatory frameworks related to AI and ML, such as GDPR, California Consumer Privacy Act, and other data privacy laws, are integral components.

This module focuses on the essential 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, emphasizing the necessity of experimentation. Learners will gain an understanding of the foundational principles of causal inference, including potential outcomes and counterfactuals. The module explores the principles and applications of A/B testing for methodically assessing the impact of interventions and changes. Students develop practical skills, designing and implementing basic A/B tests, selecting appropriate metrics, and determining sample sizes.

This module explores cutting-edge machine learning techniques and introduces the foundational concept of neural networks, emphasizing their potential as universal function approximators. Exposes learners to optimization algorithms, with a focus on practical implementation. Learners will be exposed to state-of-the-art models such as transformers and large language models (LLMs), exploring their roles in natural language processing and beyond to interpret data. Learners also engage in a mini project, applying learned concepts in a hands-on manner.

This module explores the specific data science challenges and opportunities in industries such as finance, health care, and e-commerce. Learners will grasp the unique data sources and data collection techniques relevant to their chosen sector, exploring and applying machine learning models to the industry's needs. Learners can further specialize by choosing a mini-concentration, such as finance, health care, or e-commerce, allowing for the application of 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.