Artificial intelligence is expanding to every aspect of our lives including work, learn, and play. This course describes Artificial Intelligence (AI) and compares it to data science and data analytics. It explores AI roles and the impact of AI on society. The course also explains the different types of data. The final section describes data quality.
Ai Curriculum
The AI Curriculum Modules at Texas State University are an initiative by the Center for Analytics and Data Science (CADS), developed with funding from our NSF Expand AI grant. These modules are designed to enhance AI education across disciplines, offering introductory, foundational and domain specific content. Made available through TXST Canvas, they support students and faculty in building relevant AI skills, promoting interdisciplinary learning and advancing workforce development. By exploring potential integration of these modules into courses, CADS aims to broaden access to AI knowledge and foster an inclusive environment for AI literacy across the university community.
Intro to Artificial Intelligence
Data | AI and Society
The knowledge over this explores the societal and ethical implications of data science and AI, with a focus on key principles such as fairness, accuracy, transparency, and confidentiality. It helps learners understand the importance of responsible data use and the impact of AI on individuals and communities, preparing them to make thoughtful, ethical decisions in real-world applications.
Python for AI
An introduction to Python programming with a focus on data analysis and visualization offers students learn to create and manipulate variables, work with data types, and use basic data structures like lists and dictionaries. The course covers essential programming concepts, including flow control structures (if, else, while, for) for building simple algorithms. Exploration of Python libraries, particularly Pandas, for data importation, manipulation, and analysis. Understanding the data visualization using Pandas and Matplotlib
Introduction to NLP
This course presents a practical introduction to natural language processing. It presents a foundational understanding of how text can be analyzed with statistical models and the steps required to prepare text for computer analysis. The course covers tokenization, n-grams, document-feature matrix, and includes a hands-on application to sentiment classification using the Naïve Bayes algorithm.
Introduction to PyTorch
This course offers foundational deep learning knowledge, covering key concepts, mathematical background, practical model training using PyTorch, and an overview of deep learning applications.
Introduction to Computer Vision
This course provides a comprehensive introduction to the fundamental concepts and techniques used in the field of computer vision. Students will explore how computers process and interpret visual data from the world, focusing on essential topics such as image processing, morphology, filtering, and edge detection.
Modules soon to be available
A4: Intro to Machine Learning
C1: Data, AI, and Health
C2: Data, AI, and Criminal Justice
C3: Data, AI, and Agriculture
C4: Data, AI and Education