This certificate covers the basics needed to understand and evaluate AI from a data-focused perspective. The core courses establish statistical literacy (ANLY 500) and ethical issues for AI (ANLY 620). Learners can then specialize in a specific component of artificial intelligence, including large language models (LLMs) and natural language processing (NLP) through ANLY 520 and 540, or AI and machine learning through ANLY 530 and 535.
The mission of the program is to provide a pathway for learners to build and develop the knowledge base necessary to engage with and evaluate AI models and programs for specific uses.
The Analytics AI Certificate addresses a critical skills gap by providing structured, accessible training in AI and data analytics. It responds to the growing demand for interdisciplinary talent that can bridge the gap between technical development and strategic implementation. It also supports upskilling and reskilling initiatives, making it suitable for both students preparing for the workforce and professionals pursuing career advancement or transition.
Program Goals
Identify and assess the objectives, scope, and methodological limitations for domain-specific problems
Design and execute insightful analyses
Measure, evaluate, and explain the quality of a dataset and develop a plan to improve the quality
Recognize and analyze ethical issues related to intellectual property, data security integrity, and privacy
Communicate effectively to a variety of audiences
Courses for the Certificate in Analytics AI
This certificate program requires a total of 12 semester hours. ANLY 500 and ANLY 620 are required; from there, students choose a specialized set of courses (either ANLY 530 & ANLY 535 or ANLY 520 & ANLY 540) to chart their own unique educational path.
The first course in analytics covers the core concepts and applications of analytics. The student is introduced to the main concepts and tools of analytics including descriptive, predictive, and prescriptive analytics. During the course, the student uses a variety of statistical and quantitative methods, computational tools, and predictive models to make data-driven decisions. By the end of the course, the student will apply the concepts to real work projects where, by asking some questions about an issue or situation, use analytical tools to respond to it, and present it to technical and layperson audiences.
Web technologies based on text and Natural Language Processing (NLP) are becoming the backbone of analytic solutions for understanding language as text language processing has come to play a central role in the multilingual information society. This course provides a highly accessible introduction to the field of text analytics focusing on processing text, information extraction with named entity recognition, text summarization and generation, text classification, chatbots, and deep learning toolkits. The course is a practical guide on how to create end to end systems for production in business, research, and other applied fields using *R* and Python programming languages.
This course introduces the student to machine learning. It provides the student with the cognitive, mathematical and analytical foundation required for machine learning. It also provides the student with a broad overview of machine learning, including topics from data mining, pattern recognition and supervised and unsupervised learning. This course prepares the student for the complex, higher-level topics in Principles and Applications of Deep Learning.
This course considers complex, high-level topics in machine learning. It builds on the foundation provided by Applications of Machine Learning to develop algorithms for supervised and unsupervised machine learning, to study and develop artificial neural networks, to study, develop and evaluate systems for pattern recognition and to consider trade-offs in models, for example, balancing complexity (e.g. volume, variety and velocity of big data) and performance. The focus of this course will be on more advanced techniques such as deep learning and advanced AI.
This course is an introduction to computational language models with a focus on semantic representations. Using methods such as topic models and sentiment analysis, the goal of the course is to show how insights can be derived from text focusing on semantic information. Topics range from basic visualizations of text data to text-based recommendation systems. The topics in this course have real-world applications to gaining insight into human attitudes and behaviors across domains.
This course will cover ethical concepts in data analytics and explore ongoing ethical dilemmas in the field including ethical issues related to AI. Specifically, this course will cover the legal, moral, and social implications of the analytic process from data collection to data analysis to data storage to communication and decision-making from insights. The objective of the course is to prepare the student to address the ethical challenges in the evolving field of data science.
