Data Analytics & Applications

CAPS - DATASTUDIES 5001 Foundations of Programming for Data Analytics & Applications

Programming is an increasingly important skill, whether you aspire to a career in software development or in other fields. This course introduces core programming concepts and problem-solving using Python. Students will learn the principles of software development, style, and testing. Topics include an operational model of Python execution, procedures and functions, iteration, recursion, lists, strings, algorithms, exceptions, object-oriented programming, and GUIs (graphical user interfaces). As the course progresses, students will learn to work with packages, data structures, object-oriented programming, and tools for data science and cybersecurity.

Credit 3 units.

Typical periods offered: Summer 3, Spring, Fall, Summer

CAPS - DATASTUDIES 5002 Foundations of Mathematics for Data Analytics & Applications

This course introduces the fundamental concepts, theorems, and tools used in data science and machine learning, including probability, optimization and calculus, linear algebra, discrete mathematics, and statistics. Applications of the theory to data science and machine learning will be developed with mathematical concepts being applied in Python.

Credit 3 units.

Typical periods offered: Summer 3, Spring, Fall, Summer

CAPS - DATASTUDIES 5013 Data Visualization and Story Telling

This course begins with a review of human perception and cognition, drawing upon psychological studies of perceptual accuracy and preferences. The course reviews principles of computational graphic design, what makes for a good graph, and why some data visualizations effectively present information and others do not. It considers visualization as a component of systems for data analytics and applications and presents examples of exploratory data analysis, visualizing time, networks, and maps. Students learn methods for static and interactive graphing and become familiar with tools for building web-browser-based presentations. Prerequisites: None

Credit 3 units.

Typical periods offered: Summer 3, Spring, Fall, Summer

CAPS - DATASTUDIES 5025 Enterprise Data Management

Organizations have begun generating, collecting, and accumulating more data at a faster pace than ever before. The advent of Big Data has proven to be both opportunity and challenge for contemporary organizations who are awash-even drowning-in data but starved for knowledge. Unfortunately, organizations have not developed comprehensive enterprise data management (EDM) practices that treat data as a strategic imperative. EDM is a comprehensive approach to defining, governing, securing, and maintaining the quality of all data involved in the business processes of an organization. EDM enables data-driven applications and decision-making by establishing policies and ownership of key data types and sources. The ultimate goals are to create a strategic context for the technology underpinnings of data life cycle management and ensure good stewardship of an organization's data. This course will cover the critical components of building an enterprise data management practice including, but not limited to, data strategy, data governance, data security, data architecture, data quality, data ownership, and metadata management. This course includes case studies, lectures, and group activities to enhance the textbook material.

Credit 3 units.

Typical periods offered: Spring

CAPS - DATASTUDIES 5030 Analytics Applications

This course focuses on the strategic, operational, tactical, and practical use of data analytics to inform decisions within an organization across a range of industry and government sectors as well as within organizational functions. Students will be introduced to specific analytics techniques that are used currently by practitioners in areas of diagnostic, descriptive, predictive, and prescriptive analytics. Students will learn the critical phases of analytics including data preparation, model development, evaluation, validation, selection, and deployment. In so doing, students will learn to apply data analytics in order to optimize organizational processes, improve performance, and inform decision-making.

Credit 3 units.

Typical periods offered: Spring

CAPS - DATASTUDIES 5035 Data Engineering Foundations

This course provides an overview of the discipline of data engineering. It introduces software and systems for data analytics & applications and software development as required in the design of data-intensive applications. Students learn about algorithms, data structures, and technologies for storing and processing data. Students gain experience with open-source software, text editors, integrated development environments, and cloud systems. Students employ best practices in software development, utilizing tools for syntax checking, testing, debugging, and version control. The course also introduces formal models for algorithm and system performance.

Credit 3 units.

Typical periods offered: Fall, Spring

CAPS - DATASTUDIES 5040 Applied Machine Learning

This course introduces machine learning with business applications. It provides a survey of statistical and machine learning algorithms and techniques including the machine learning framework; regression; classification; regularization and reduction; tree-based methods; unsupervised learning; and fully connected, convolutional, and recurrent neural networks. Students implement machine learning models with open-source software for data analytics and applications. They explore data and learn from data, finding underlying patterns useful for data reduction, feature analysis, prediction, and classification.

Credit 3 units.

Typical periods offered: Fall, Summer 3, Spring

CAPS - DATASTUDIES 5045 Applications of Deep Neural Networks

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity with at least one programming language is assumed.

Credit 3 units.

Typical periods offered: Fall, Summer 3, Spring

CAPS - DATASTUDIES 5300 Introduction to Relational Databases and SQL Programming

The purpose of this course is to introduce the essential concepts behind relational databases, and help students acquire and apply foundational knowledge of the SQL language and Relational Database Management Systems (RDBMS). Students will study relational data models and discover how they are created and what benefits they bring, plus how to apply them to their own data. Additionally, students are exposed to other types of datastores like NoSQL and graph databases, and how to work with them. The emphasis in this course is on practical and hands-on learning. Through a series of labs, students will practice building and running SQL queries.

Credit 3 units.

Typical periods offered: Summer 3, Spring, Fall, Summer

CAPS - DATASTUDIES 5740 Foundations of Data Analytics

Organizations are rapidly transforming the way they ingest, integrate, store, and serve data and perform analytics. In this course, students will learn the steps involved with designing and implementing data analytics and applications projects. Topics addressed include ingesting and parsing data from various sources, dealing with messy and missing data, transforming and engineering features, building and evaluating models, and visualizing results. Using Python, as well as other tools, students will complete assignments learning the process of building a data model as well as a variety of analytics techniques and under what situations they are best employed. Through lectures and practical exercises, students will become familiar with the computational mathematics that underpin analytics; the elements of statistical modeling and machine learning; model interpretation and assessment; and structured and unstructured data analysis.

Credit 3 units.

Typical periods offered: Summer 3, Spring, Fall, Summer

CAPS - DATASTUDIES 5750 AI/ML Ops

This course provides students with an advanced understanding of artificial intelligence (AI) and machine learning operations (MLOps). It focuses on the end-to-end lifecycle of AI/ML models, from development to production, covering data engineering, model training, deployment, monitoring, and continuous integration/continuous deployment (CI/CD). Students will learn best practices for scaling AI/ML workflows, building pipelines, monitoring models in production, and managing versioning and reproducibility. Students taking this course should have foundational knowledge of AI/ML algorithms, basic programming skills (Python, Bash scripting), and introductory knowledge of DevOps (containers, CI/CD pipelines). A working knowledge of Databricks Community Edition would also be helpful.

Credit 3 units.

Typical periods offered: Summer 3, Spring, Fall, Summer