Curriculum for Data Analytics & Artificial Intelligence Degree

There is a difference between what science is, its history and meaning, and what science does through experimental and applied practices. This interdisciplinary course invites students to examine how scientific knowledge is defined, constructed, and communicated. Students will critically analyze different examples of science in society to understand how science shapes and is shaped by culture, power, and people. By the end of the course, students will be equipped to evaluate the ways in which science reflects and transforms the world around us.

This course equips students with interdisciplinary tools and methods to analyze and respond to complex, real-world problems. Drawing on diverse frameworks and theories, students will explore how their own identities and experiences shape their understanding of contemporary issues. Emphasis is placed on synthesizing knowledge across fields to develop innovative, contextually relevant solutions. Students will communicate their insights effectively through a range of expressive modes tailored to varied audiences and purposes.

Students will develop a statistical toolkit for navigating an uncertain world. From summarizing data and understanding probability to testing hypotheses and modeling relationships between variables, students will gain practical skills for making evidence-based decisions. Through applying statistical methods to real community issues and challenges at various scales, students will learn to integrate quantitative reasoning with broader interdisciplinary perspectives, building the analytical and social awareness necessary for responsible engagement with complex problems in their personal, professional, and civic communities.

Teaches the practical side of exploring, presenting, and analyzing data. Students will learn to effectively use a statistical software package to accomplish these tasks. After learning to graphically display data in meaningful ways, students will learn to analyze data, test claims, and make valid conclusions. These analyses will include hypothesis tests involving more than one sample, linear and multiple linear regressions, chi-squared tests of independence and goodness of fit, and ANOVA.

This course will introduce students with a background in Calculus to the fields of probability and statistics. Topics include discrete and continuous probability, key terms and theorems, and different statistical distributions and models. Students will work with concepts by-hand and with technology in order to develop a greater understanding of the structure and applications of probability and statistics as a whole.

Students will analyze data using Python programming language to obtain insights from real-world data sets. Students will access data files containing both numerical and categorical data, which they will prepare for analysis by data preprocessing, data wrangling, and data aggregation. Students will conduct statistical analysis, text analysis, and date/time series analysis, summarize their findings in a meaningful manner, and use visualization to illustrate patterns and trends in the data.

Data visualization-representing complex data in a visual format-is a way to communicate and explore patterns, trends, and relationships in a data set. In this course, students will examine a variety of data sets to create data visualizations and use them to explore, interpret, and present information. After transforming data sets into visual graphics, students will then create business reports and presentations designed to drive effective decision-making.

Students learn the history and basics of computing, as well as the fundamentals of programming. General topics include: the history of computing, number systems and classical computing, Boolean logic, algorithm design and implementation, and modern computer organization. Programming topics include: memory and variables, mathematical operations, basic input and output, decision making, repetitions, functions, and arrays.

This course builds on the content from Introduction to Programming. Students study the Object Oriented Programming (OOP) Paradigm with topics such as objects, classes, encapsulation, abstraction, modularity, inheritance, and polymorphism. Students examine and use structures such as arrays, structs, classes, and linked lists to model complex information. Pointers and dynamic memory allocation are covered, as well as principles such as overloading and overriding. Students work to solve problems by selecting implementation options from competing alternatives.

Students compare and contrast a variety of data structures. Students compare algorithms for tasks such as searching and sorting, while articulating efficiency in terms of time complexity. Students implement data structures and algorithms to support solution designs.

This course will introduce students to database design, Structured Query Language (SQL), normalization, and relational database theory. Traditional relational databases will be contrasted with NoSQL paradigm databases including document-oriented, key-value store, and graph. Students will gain hands-on experience writing database applications.

This course introduces students to data mining and its various applications. Students will gain proficiency in supervised data mining techniques for building prediction models such as decision trees, random decision forests, bootstrapping, training and testing using multi-fold cross-validation and using entropy measures for weighting features. Students will also use unsupervised data mining techniques such as clustering and association analysis. Both methods are used for discovering patterns and associations in data.