Data Analytics

This course introduces foundational concepts in data and artificial intelligence, including how data is structured, analyzed, and applied in modern AI systems. Students develop essential data and AI literacy while exploring issues of fairness, privacy, and responsible use. Course activities focus on interpreting datasets, examining AI applications and prompt engineering, and evaluating ethical implications of data quality and AI deployment.

This course introduces programming for data analysis through automation, reproducibility, and AI-assisted development. Students learn how to write, adapt, and debug code using current data programming languages (such as R and Python) and integrated AI tools. Rather than focusing on manual syntax memorization, the course emphasizes efficient problem-solving, assisted programming, and workflow automation.

This course introduces techniques for discovering patterns and insights in data. Topics include clustering, classification, and association analysis. The connection between data mining techniques and machine learning and artificial intelligence will be examined. Students will practice applying these methods to real-world datasets and communicate findings effectively.

In this course students will apply basic statistical and data mining techniques to work with large scale datasets. Structured and unstructured data will be used. Case studies of large-scale analysis in practice will be reviewed. Topics include summarizing large datasets, analyzing text data, methods of modelling with data, and analyzing data over time. Students use a statistical programming language. 

This course covers the fundamental concepts of database systems. Students will focus on two of the most common types of databases, SQL or relational and NoSQL or document-based databases. The course covers preparation of databases, how to import data efficiently and techniques to query and aggregate the data for analysis. The applications of different database types will be compared as well as important considerations when choosing databases and tools.

This course focuses on using AI tools to design and implement automated data-driven business processes. Students learn to connect analytics, reporting, and operational systems using AI copilots, APIs, and workflow platforms. Emphasis is on building intelligent, reliable systems that reduce manual work, improve accuracy, and support continuous decision-making while addressing ethical use, bias, and human oversight in automation. 

This course focuses on transforming data into meaningful, accessible insights for diverse audiences. Students will learn principles of data communication, including design, storytelling, and ethical representation. Emphasis is placed on creating outputs that are clear, interpretable, and ADA-compliant, including visual, textual, and alternative formats. Learners will gain hands-on experience with static and interactive data presentations, using spreadsheet tools, no-code platforms, and AI-assisted design tools. The course also explores accessibility best practices, ethical considerations, and audience-centered communication strategies.

This course focuses on preparing datasets for AI applications and data analysis. Students will learn to clean, filter, and reshape data; identify and correct missing or inconsistent entries; and apply techniques such as dimensionality reduction to produce high-quality datasets for modeling. Structured Query Language (SQL) will be taught and used for data selection, summarization, filtering, merging, and subsetting. Tools and technologies include spreadsheet software as well as R and Python libraries for data manipulation and analysis. 

This course builds on data mining concepts and studies how to apply machine learning and natural language processing (NLP) techniques in real-world business and organizational contexts. Students will develop predictive and prescriptive models for forecasting, classification, and optimization, and apply NLP methods such as sentiment analysis and topic extraction to unstructured text. Emphasis is on practical implementation, model evaluation, and translating AI-driven insights into data-informed decisions. 

Teaches students to analyze data to answer real business questions and support organizational decisions, incorporating insights from AI-augmented analytics. Students apply descriptive, diagnostic, and basic predictive methods such as time series, forecasting, and simple statistical modeling to real-world business problems. The course emphasizes interpreting results, communicating insights clearly to stakeholders, and understanding the analytical needs of a business context. Students apply concepts through case studies using AI-assisted insights to reinforce practical decision-making skills.